Merge pi-dev-plus-aosp-without-vendor into stage-aosp-master
Bug: 79597307
Change-Id: I56180e0541e15516ddb3cbb794f2693cd15ff220
diff --git a/Android.mk b/Android.mk
index 2d19e3d..4e5bbf5 100644
--- a/Android.mk
+++ b/Android.mk
@@ -36,27 +36,26 @@
MY_LIBTEXTCLASSIFIER_CFLAGS := \
$(MY_LIBTEXTCLASSIFIER_WARNING_CFLAGS) \
- -fvisibility=hidden
+ -fvisibility=hidden \
+ -DLIBTEXTCLASSIFIER_UNILIB_ICU \
+ -DZLIB_CONST
# Only enable debug logging in userdebug/eng builds.
ifneq (,$(filter userdebug eng, $(TARGET_BUILD_VARIANT)))
MY_LIBTEXTCLASSIFIER_CFLAGS += -DTC_DEBUG_LOGGING=1
endif
-# ------------------------
-# libtextclassifier_protos
-# ------------------------
+# -----------------
+# flatbuffers
+# -----------------
+# Empty static library so that other projects can include just the basic
+# FlatBuffers headers as a module.
include $(CLEAR_VARS)
-
-LOCAL_MODULE := libtextclassifier_protos
-
-LOCAL_STRIP_MODULE := $(LIBTEXTCLASSIFIER_STRIP_OPTS)
-
-LOCAL_SRC_FILES := $(call all-proto-files-under, .)
-LOCAL_SHARED_LIBRARIES := libprotobuf-cpp-lite
-
-LOCAL_CFLAGS := $(MY_LIBTEXTCLASSIFIER_WARNING_CFLAGS)
+LOCAL_MODULE := flatbuffers
+LOCAL_EXPORT_C_INCLUDES := $(LOCAL_PATH)/include
+LOCAL_EXPORT_CPPFLAGS := -std=c++11 -fexceptions -Wall \
+ -DFLATBUFFERS_TRACK_VERIFIER_BUFFER_SIZE
include $(BUILD_STATIC_LIBRARY)
@@ -67,23 +66,31 @@
include $(CLEAR_VARS)
LOCAL_MODULE := libtextclassifier
-proto_sources_dir := $(generated_sources_dir)
-
LOCAL_CPP_EXTENSION := .cc
LOCAL_CFLAGS += $(MY_LIBTEXTCLASSIFIER_CFLAGS)
LOCAL_STRIP_MODULE := $(LIBTEXTCLASSIFIER_STRIP_OPTS)
-LOCAL_SRC_FILES := $(filter-out tests/% %_test.cc,$(call all-subdir-cpp-files))
-LOCAL_C_INCLUDES += $(proto_sources_dir)/proto/external/libtextclassifier
+LOCAL_SRC_FILES := $(filter-out tests/% %_test.cc test-util.%,$(call all-subdir-cpp-files))
-LOCAL_STATIC_LIBRARIES += libtextclassifier_protos
-LOCAL_SHARED_LIBRARIES += libprotobuf-cpp-lite
+LOCAL_C_INCLUDES := $(TOP)/external/zlib
+LOCAL_C_INCLUDES += $(TOP)/external/tensorflow
+LOCAL_C_INCLUDES += $(TOP)/external/flatbuffers/include
+
LOCAL_SHARED_LIBRARIES += liblog
-LOCAL_SHARED_LIBRARIES += libicuuc libicui18n
-LOCAL_REQUIRED_MODULES := textclassifier.smartselection.en.model
+LOCAL_SHARED_LIBRARIES += libicuuc
+LOCAL_SHARED_LIBRARIES += libicui18n
+LOCAL_SHARED_LIBRARIES += libtflite
+LOCAL_SHARED_LIBRARIES += libz
+
+LOCAL_STATIC_LIBRARIES += flatbuffers
+
+LOCAL_REQUIRED_MODULES := textclassifier.en.model
+LOCAL_REQUIRED_MODULES += textclassifier.universal.model
LOCAL_ADDITIONAL_DEPENDENCIES += $(LOCAL_PATH)/jni.lds
LOCAL_LDFLAGS += -Wl,-version-script=$(LOCAL_PATH)/jni.lds
+LOCAL_CPPFLAGS_32 += -DLIBTEXTCLASSIFIER_TEST_DATA_DIR="\"/data/nativetest/libtextclassifier_tests/test_data/\""
+LOCAL_CPPFLAGS_64 += -DLIBTEXTCLASSIFIER_TEST_DATA_DIR="\"/data/nativetest64/libtextclassifier_tests/test_data/\""
include $(BUILD_SHARED_LIBRARY)
@@ -101,162 +108,45 @@
LOCAL_CFLAGS += $(MY_LIBTEXTCLASSIFIER_CFLAGS)
LOCAL_STRIP_MODULE := $(LIBTEXTCLASSIFIER_STRIP_OPTS)
-LOCAL_TEST_DATA := $(call find-test-data-in-subdirs, $(LOCAL_PATH), *, tests/testdata)
+LOCAL_TEST_DATA := $(call find-test-data-in-subdirs, $(LOCAL_PATH), *, test_data)
-LOCAL_CPPFLAGS_32 += -DTEST_DATA_DIR="\"/data/nativetest/libtextclassifier_tests/tests/testdata/\""
-LOCAL_CPPFLAGS_64 += -DTEST_DATA_DIR="\"/data/nativetest64/libtextclassifier_tests/tests/testdata/\""
+LOCAL_CPPFLAGS_32 += -DLIBTEXTCLASSIFIER_TEST_DATA_DIR="\"/data/nativetest/libtextclassifier_tests/test_data/\""
+LOCAL_CPPFLAGS_64 += -DLIBTEXTCLASSIFIER_TEST_DATA_DIR="\"/data/nativetest64/libtextclassifier_tests/test_data/\""
LOCAL_SRC_FILES := $(call all-subdir-cpp-files)
-LOCAL_C_INCLUDES += $(proto_sources_dir)/proto/external/libtextclassifier
-LOCAL_STATIC_LIBRARIES += libtextclassifier_protos libgmock
-LOCAL_SHARED_LIBRARIES += libprotobuf-cpp-lite
+LOCAL_C_INCLUDES := $(TOP)/external/zlib
+LOCAL_C_INCLUDES += $(TOP)/external/tensorflow
+LOCAL_C_INCLUDES += $(TOP)/external/flatbuffers/include
+
+LOCAL_STATIC_LIBRARIES += libgmock
LOCAL_SHARED_LIBRARIES += liblog
-LOCAL_SHARED_LIBRARIES += libicuuc libicui18n
+LOCAL_SHARED_LIBRARIES += libicuuc
+LOCAL_SHARED_LIBRARIES += libicui18n
+LOCAL_SHARED_LIBRARIES += libtflite
+LOCAL_SHARED_LIBRARIES += libz
+
+LOCAL_STATIC_LIBRARIES += flatbuffers
include $(BUILD_NATIVE_TEST)
-# ------------
-# LangId model
-# ------------
-
-include $(CLEAR_VARS)
-LOCAL_MODULE := textclassifier.langid.model
-LOCAL_MODULE_CLASS := ETC
-LOCAL_MODULE_OWNER := google
-LOCAL_SRC_FILES := ./models/textclassifier.langid.model
-LOCAL_MODULE_PATH := $(TARGET_OUT_ETC)/textclassifier
-include $(BUILD_PREBUILT)
-
# ----------------------
# Smart Selection models
# ----------------------
include $(CLEAR_VARS)
-LOCAL_MODULE := textclassifier.smartselection.ar.model
+LOCAL_MODULE := textclassifier.en.model
LOCAL_MODULE_CLASS := ETC
LOCAL_MODULE_OWNER := google
-LOCAL_SRC_FILES := ./models/textclassifier.smartselection.ar.model
+LOCAL_SRC_FILES := ./models/textclassifier.en.model
LOCAL_MODULE_PATH := $(TARGET_OUT_ETC)/textclassifier
include $(BUILD_PREBUILT)
include $(CLEAR_VARS)
-LOCAL_MODULE := textclassifier.smartselection.de.model
+LOCAL_MODULE := textclassifier.universal.model
LOCAL_MODULE_CLASS := ETC
LOCAL_MODULE_OWNER := google
-LOCAL_SRC_FILES := ./models/textclassifier.smartselection.de.model
-LOCAL_MODULE_PATH := $(TARGET_OUT_ETC)/textclassifier
-include $(BUILD_PREBUILT)
-
-include $(CLEAR_VARS)
-LOCAL_MODULE := textclassifier.smartselection.en.model
-LOCAL_MODULE_CLASS := ETC
-LOCAL_MODULE_OWNER := google
-LOCAL_SRC_FILES := ./models/textclassifier.smartselection.en.model
-LOCAL_MODULE_PATH := $(TARGET_OUT_ETC)/textclassifier
-include $(BUILD_PREBUILT)
-
-include $(CLEAR_VARS)
-LOCAL_MODULE := textclassifier.smartselection.es.model
-LOCAL_MODULE_CLASS := ETC
-LOCAL_MODULE_OWNER := google
-LOCAL_SRC_FILES := ./models/textclassifier.smartselection.es.model
-LOCAL_MODULE_PATH := $(TARGET_OUT_ETC)/textclassifier
-include $(BUILD_PREBUILT)
-
-include $(CLEAR_VARS)
-LOCAL_MODULE := textclassifier.smartselection.fr.model
-LOCAL_MODULE_CLASS := ETC
-LOCAL_MODULE_OWNER := google
-LOCAL_SRC_FILES := ./models/textclassifier.smartselection.fr.model
-LOCAL_MODULE_PATH := $(TARGET_OUT_ETC)/textclassifier
-include $(BUILD_PREBUILT)
-
-include $(CLEAR_VARS)
-LOCAL_MODULE := textclassifier.smartselection.it.model
-LOCAL_MODULE_CLASS := ETC
-LOCAL_MODULE_OWNER := google
-LOCAL_SRC_FILES := ./models/textclassifier.smartselection.it.model
-LOCAL_MODULE_PATH := $(TARGET_OUT_ETC)/textclassifier
-include $(BUILD_PREBUILT)
-
-include $(CLEAR_VARS)
-LOCAL_MODULE := textclassifier.smartselection.ja.model
-LOCAL_MODULE_CLASS := ETC
-LOCAL_MODULE_OWNER := google
-LOCAL_SRC_FILES := ./models/textclassifier.smartselection.ja.model
-LOCAL_MODULE_PATH := $(TARGET_OUT_ETC)/textclassifier
-include $(BUILD_PREBUILT)
-
-include $(CLEAR_VARS)
-LOCAL_MODULE := textclassifier.smartselection.ko.model
-LOCAL_MODULE_CLASS := ETC
-LOCAL_MODULE_OWNER := google
-LOCAL_SRC_FILES := ./models/textclassifier.smartselection.ko.model
-LOCAL_MODULE_PATH := $(TARGET_OUT_ETC)/textclassifier
-include $(BUILD_PREBUILT)
-
-include $(CLEAR_VARS)
-LOCAL_MODULE := textclassifier.smartselection.nl.model
-LOCAL_MODULE_CLASS := ETC
-LOCAL_MODULE_OWNER := google
-LOCAL_SRC_FILES := ./models/textclassifier.smartselection.nl.model
-LOCAL_MODULE_PATH := $(TARGET_OUT_ETC)/textclassifier
-include $(BUILD_PREBUILT)
-
-include $(CLEAR_VARS)
-LOCAL_MODULE := textclassifier.smartselection.pl.model
-LOCAL_MODULE_CLASS := ETC
-LOCAL_MODULE_OWNER := google
-LOCAL_SRC_FILES := ./models/textclassifier.smartselection.pl.model
-LOCAL_MODULE_PATH := $(TARGET_OUT_ETC)/textclassifier
-include $(BUILD_PREBUILT)
-
-include $(CLEAR_VARS)
-LOCAL_MODULE := textclassifier.smartselection.pt.model
-LOCAL_MODULE_CLASS := ETC
-LOCAL_MODULE_OWNER := google
-LOCAL_SRC_FILES := ./models/textclassifier.smartselection.pt.model
-LOCAL_MODULE_PATH := $(TARGET_OUT_ETC)/textclassifier
-include $(BUILD_PREBUILT)
-
-include $(CLEAR_VARS)
-LOCAL_MODULE := textclassifier.smartselection.ru.model
-LOCAL_MODULE_CLASS := ETC
-LOCAL_MODULE_OWNER := google
-LOCAL_SRC_FILES := ./models/textclassifier.smartselection.ru.model
-LOCAL_MODULE_PATH := $(TARGET_OUT_ETC)/textclassifier
-include $(BUILD_PREBUILT)
-
-include $(CLEAR_VARS)
-LOCAL_MODULE := textclassifier.smartselection.th.model
-LOCAL_MODULE_CLASS := ETC
-LOCAL_MODULE_OWNER := google
-LOCAL_SRC_FILES := ./models/textclassifier.smartselection.th.model
-LOCAL_MODULE_PATH := $(TARGET_OUT_ETC)/textclassifier
-include $(BUILD_PREBUILT)
-
-include $(CLEAR_VARS)
-LOCAL_MODULE := textclassifier.smartselection.tr.model
-LOCAL_MODULE_CLASS := ETC
-LOCAL_MODULE_OWNER := google
-LOCAL_SRC_FILES := ./models/textclassifier.smartselection.tr.model
-LOCAL_MODULE_PATH := $(TARGET_OUT_ETC)/textclassifier
-include $(BUILD_PREBUILT)
-
-include $(CLEAR_VARS)
-LOCAL_MODULE := textclassifier.smartselection.zh-Hant.model
-LOCAL_MODULE_CLASS := ETC
-LOCAL_MODULE_OWNER := google
-LOCAL_SRC_FILES := ./models/textclassifier.smartselection.zh-Hant.model
-LOCAL_MODULE_PATH := $(TARGET_OUT_ETC)/textclassifier
-include $(BUILD_PREBUILT)
-
-include $(CLEAR_VARS)
-LOCAL_MODULE := textclassifier.smartselection.zh.model
-LOCAL_MODULE_CLASS := ETC
-LOCAL_MODULE_OWNER := google
-LOCAL_SRC_FILES := ./models/textclassifier.smartselection.zh.model
+LOCAL_SRC_FILES := ./models/textclassifier.universal.model
LOCAL_MODULE_PATH := $(TARGET_OUT_ETC)/textclassifier
include $(BUILD_PREBUILT)
@@ -265,10 +155,7 @@
# -----------------------
include $(CLEAR_VARS)
-LOCAL_MODULE := textclassifier.smartselection.bundle1
-LOCAL_REQUIRED_MODULES := textclassifier.smartselection.en.model
-LOCAL_REQUIRED_MODULES += textclassifier.smartselection.es.model
-LOCAL_REQUIRED_MODULES += textclassifier.smartselection.de.model
-LOCAL_REQUIRED_MODULES += textclassifier.smartselection.fr.model
+LOCAL_MODULE := textclassifier.bundle1
+LOCAL_REQUIRED_MODULES := textclassifier.en.model
LOCAL_CFLAGS := $(MY_LIBTEXTCLASSIFIER_WARNING_CFLAGS)
include $(BUILD_STATIC_LIBRARY)
diff --git a/cached-features.cc b/cached-features.cc
new file mode 100644
index 0000000..2a46780
--- /dev/null
+++ b/cached-features.cc
@@ -0,0 +1,173 @@
+/*
+ * Copyright (C) 2017 The Android Open Source Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "cached-features.h"
+
+#include "tensor-view.h"
+#include "util/base/logging.h"
+
+namespace libtextclassifier2 {
+
+namespace {
+
+int CalculateOutputFeaturesSize(const FeatureProcessorOptions* options,
+ int feature_vector_size) {
+ const bool bounds_sensitive_enabled =
+ options->bounds_sensitive_features() &&
+ options->bounds_sensitive_features()->enabled();
+
+ int num_extracted_tokens = 0;
+ if (bounds_sensitive_enabled) {
+ const FeatureProcessorOptions_::BoundsSensitiveFeatures* config =
+ options->bounds_sensitive_features();
+ num_extracted_tokens += config->num_tokens_before();
+ num_extracted_tokens += config->num_tokens_inside_left();
+ num_extracted_tokens += config->num_tokens_inside_right();
+ num_extracted_tokens += config->num_tokens_after();
+ if (config->include_inside_bag()) {
+ ++num_extracted_tokens;
+ }
+ } else {
+ num_extracted_tokens = 2 * options->context_size() + 1;
+ }
+
+ int output_features_size = num_extracted_tokens * feature_vector_size;
+
+ if (bounds_sensitive_enabled &&
+ options->bounds_sensitive_features()->include_inside_length()) {
+ ++output_features_size;
+ }
+
+ return output_features_size;
+}
+
+} // namespace
+
+std::unique_ptr<CachedFeatures> CachedFeatures::Create(
+ const TokenSpan& extraction_span,
+ std::unique_ptr<std::vector<float>> features,
+ std::unique_ptr<std::vector<float>> padding_features,
+ const FeatureProcessorOptions* options, int feature_vector_size) {
+ const int min_feature_version =
+ options->bounds_sensitive_features() &&
+ options->bounds_sensitive_features()->enabled()
+ ? 2
+ : 1;
+ if (options->feature_version() < min_feature_version) {
+ TC_LOG(ERROR) << "Unsupported feature version.";
+ return nullptr;
+ }
+
+ std::unique_ptr<CachedFeatures> cached_features(new CachedFeatures());
+ cached_features->extraction_span_ = extraction_span;
+ cached_features->features_ = std::move(features);
+ cached_features->padding_features_ = std::move(padding_features);
+ cached_features->options_ = options;
+
+ cached_features->output_features_size_ =
+ CalculateOutputFeaturesSize(options, feature_vector_size);
+
+ return cached_features;
+}
+
+void CachedFeatures::AppendClickContextFeaturesForClick(
+ int click_pos, std::vector<float>* output_features) const {
+ click_pos -= extraction_span_.first;
+
+ AppendFeaturesInternal(
+ /*intended_span=*/ExpandTokenSpan(SingleTokenSpan(click_pos),
+ options_->context_size(),
+ options_->context_size()),
+ /*read_mask_span=*/{0, TokenSpanSize(extraction_span_)}, output_features);
+}
+
+void CachedFeatures::AppendBoundsSensitiveFeaturesForSpan(
+ TokenSpan selected_span, std::vector<float>* output_features) const {
+ const FeatureProcessorOptions_::BoundsSensitiveFeatures* config =
+ options_->bounds_sensitive_features();
+
+ selected_span.first -= extraction_span_.first;
+ selected_span.second -= extraction_span_.first;
+
+ // Append the features for tokens around the left bound. Masks out tokens
+ // after the right bound, so that if num_tokens_inside_left goes past it,
+ // padding tokens will be used.
+ AppendFeaturesInternal(
+ /*intended_span=*/{selected_span.first - config->num_tokens_before(),
+ selected_span.first +
+ config->num_tokens_inside_left()},
+ /*read_mask_span=*/{0, selected_span.second}, output_features);
+
+ // Append the features for tokens around the right bound. Masks out tokens
+ // before the left bound, so that if num_tokens_inside_right goes past it,
+ // padding tokens will be used.
+ AppendFeaturesInternal(
+ /*intended_span=*/{selected_span.second -
+ config->num_tokens_inside_right(),
+ selected_span.second + config->num_tokens_after()},
+ /*read_mask_span=*/{selected_span.first, TokenSpanSize(extraction_span_)},
+ output_features);
+
+ if (config->include_inside_bag()) {
+ AppendBagFeatures(selected_span, output_features);
+ }
+
+ if (config->include_inside_length()) {
+ output_features->push_back(
+ static_cast<float>(TokenSpanSize(selected_span)));
+ }
+}
+
+void CachedFeatures::AppendFeaturesInternal(
+ const TokenSpan& intended_span, const TokenSpan& read_mask_span,
+ std::vector<float>* output_features) const {
+ const TokenSpan copy_span =
+ IntersectTokenSpans(intended_span, read_mask_span);
+ for (int i = intended_span.first; i < copy_span.first; ++i) {
+ AppendPaddingFeatures(output_features);
+ }
+ output_features->insert(
+ output_features->end(),
+ features_->begin() + copy_span.first * NumFeaturesPerToken(),
+ features_->begin() + copy_span.second * NumFeaturesPerToken());
+ for (int i = copy_span.second; i < intended_span.second; ++i) {
+ AppendPaddingFeatures(output_features);
+ }
+}
+
+void CachedFeatures::AppendPaddingFeatures(
+ std::vector<float>* output_features) const {
+ output_features->insert(output_features->end(), padding_features_->begin(),
+ padding_features_->end());
+}
+
+void CachedFeatures::AppendBagFeatures(
+ const TokenSpan& bag_span, std::vector<float>* output_features) const {
+ const int offset = output_features->size();
+ output_features->resize(output_features->size() + NumFeaturesPerToken());
+ for (int i = bag_span.first; i < bag_span.second; ++i) {
+ for (int j = 0; j < NumFeaturesPerToken(); ++j) {
+ (*output_features)[offset + j] +=
+ (*features_)[i * NumFeaturesPerToken() + j] / TokenSpanSize(bag_span);
+ }
+ }
+}
+
+int CachedFeatures::NumFeaturesPerToken() const {
+ return padding_features_->size();
+}
+
+} // namespace libtextclassifier2
diff --git a/cached-features.h b/cached-features.h
new file mode 100644
index 0000000..0224d86
--- /dev/null
+++ b/cached-features.h
@@ -0,0 +1,83 @@
+/*
+ * Copyright (C) 2017 The Android Open Source Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#ifndef LIBTEXTCLASSIFIER_CACHED_FEATURES_H_
+#define LIBTEXTCLASSIFIER_CACHED_FEATURES_H_
+
+#include <memory>
+#include <vector>
+
+#include "model-executor.h"
+#include "model_generated.h"
+#include "types.h"
+
+namespace libtextclassifier2 {
+
+// Holds state for extracting features across multiple calls and reusing them.
+// Assumes that features for each Token are independent.
+class CachedFeatures {
+ public:
+ static std::unique_ptr<CachedFeatures> Create(
+ const TokenSpan& extraction_span,
+ std::unique_ptr<std::vector<float>> features,
+ std::unique_ptr<std::vector<float>> padding_features,
+ const FeatureProcessorOptions* options, int feature_vector_size);
+
+ // Appends the click context features for the given click position to
+ // 'output_features'.
+ void AppendClickContextFeaturesForClick(
+ int click_pos, std::vector<float>* output_features) const;
+
+ // Appends the bounds-sensitive features for the given token span to
+ // 'output_features'.
+ void AppendBoundsSensitiveFeaturesForSpan(
+ TokenSpan selected_span, std::vector<float>* output_features) const;
+
+ // Returns number of features that 'AppendFeaturesForSpan' appends.
+ int OutputFeaturesSize() const { return output_features_size_; }
+
+ private:
+ CachedFeatures() {}
+
+ // Appends token features to the output. The intended_span specifies which
+ // tokens' features should be used in principle. The read_mask_span restricts
+ // which tokens are actually read. For tokens outside of the read_mask_span,
+ // padding tokens are used instead.
+ void AppendFeaturesInternal(const TokenSpan& intended_span,
+ const TokenSpan& read_mask_span,
+ std::vector<float>* output_features) const;
+
+ // Appends features of one padding token to the output.
+ void AppendPaddingFeatures(std::vector<float>* output_features) const;
+
+ // Appends the features of tokens from the given span to the output. The
+ // features are averaged so that the appended features have the size
+ // corresponding to one token.
+ void AppendBagFeatures(const TokenSpan& bag_span,
+ std::vector<float>* output_features) const;
+
+ int NumFeaturesPerToken() const;
+
+ TokenSpan extraction_span_;
+ const FeatureProcessorOptions* options_;
+ int output_features_size_;
+ std::unique_ptr<std::vector<float>> features_;
+ std::unique_ptr<std::vector<float>> padding_features_;
+};
+
+} // namespace libtextclassifier2
+
+#endif // LIBTEXTCLASSIFIER_CACHED_FEATURES_H_
diff --git a/cached-features_test.cc b/cached-features_test.cc
new file mode 100644
index 0000000..f064a63
--- /dev/null
+++ b/cached-features_test.cc
@@ -0,0 +1,157 @@
+/*
+ * Copyright (C) 2017 The Android Open Source Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "cached-features.h"
+
+#include "model-executor.h"
+#include "tensor-view.h"
+
+#include "gmock/gmock.h"
+#include "gtest/gtest.h"
+
+using testing::ElementsAreArray;
+using testing::FloatEq;
+using testing::Matcher;
+
+namespace libtextclassifier2 {
+namespace {
+
+Matcher<std::vector<float>> ElementsAreFloat(const std::vector<float>& values) {
+ std::vector<Matcher<float>> matchers;
+ for (const float value : values) {
+ matchers.push_back(FloatEq(value));
+ }
+ return ElementsAreArray(matchers);
+}
+
+std::unique_ptr<std::vector<float>> MakeFeatures(int num_tokens) {
+ std::unique_ptr<std::vector<float>> features(new std::vector<float>());
+ for (int i = 1; i <= num_tokens; ++i) {
+ features->push_back(i * 11.0f);
+ features->push_back(-i * 11.0f);
+ features->push_back(i * 0.1f);
+ }
+ return features;
+}
+
+std::vector<float> GetCachedClickContextFeatures(
+ const CachedFeatures& cached_features, int click_pos) {
+ std::vector<float> output_features;
+ cached_features.AppendClickContextFeaturesForClick(click_pos,
+ &output_features);
+ return output_features;
+}
+
+std::vector<float> GetCachedBoundsSensitiveFeatures(
+ const CachedFeatures& cached_features, TokenSpan selected_span) {
+ std::vector<float> output_features;
+ cached_features.AppendBoundsSensitiveFeaturesForSpan(selected_span,
+ &output_features);
+ return output_features;
+}
+
+TEST(CachedFeaturesTest, ClickContext) {
+ FeatureProcessorOptionsT options;
+ options.context_size = 2;
+ options.feature_version = 1;
+ flatbuffers::FlatBufferBuilder builder;
+ builder.Finish(CreateFeatureProcessorOptions(builder, &options));
+ flatbuffers::DetachedBuffer options_fb = builder.Release();
+
+ std::unique_ptr<std::vector<float>> features = MakeFeatures(9);
+ std::unique_ptr<std::vector<float>> padding_features(
+ new std::vector<float>{112233.0, -112233.0, 321.0});
+
+ const std::unique_ptr<CachedFeatures> cached_features =
+ CachedFeatures::Create(
+ {3, 10}, std::move(features), std::move(padding_features),
+ flatbuffers::GetRoot<FeatureProcessorOptions>(options_fb.data()),
+ /*feature_vector_size=*/3);
+ ASSERT_TRUE(cached_features);
+
+ EXPECT_THAT(GetCachedClickContextFeatures(*cached_features, 5),
+ ElementsAreFloat({11.0, -11.0, 0.1, 22.0, -22.0, 0.2, 33.0, -33.0,
+ 0.3, 44.0, -44.0, 0.4, 55.0, -55.0, 0.5}));
+
+ EXPECT_THAT(GetCachedClickContextFeatures(*cached_features, 6),
+ ElementsAreFloat({22.0, -22.0, 0.2, 33.0, -33.0, 0.3, 44.0, -44.0,
+ 0.4, 55.0, -55.0, 0.5, 66.0, -66.0, 0.6}));
+
+ EXPECT_THAT(GetCachedClickContextFeatures(*cached_features, 7),
+ ElementsAreFloat({33.0, -33.0, 0.3, 44.0, -44.0, 0.4, 55.0, -55.0,
+ 0.5, 66.0, -66.0, 0.6, 77.0, -77.0, 0.7}));
+}
+
+TEST(CachedFeaturesTest, BoundsSensitive) {
+ std::unique_ptr<FeatureProcessorOptions_::BoundsSensitiveFeaturesT> config(
+ new FeatureProcessorOptions_::BoundsSensitiveFeaturesT());
+ config->enabled = true;
+ config->num_tokens_before = 2;
+ config->num_tokens_inside_left = 2;
+ config->num_tokens_inside_right = 2;
+ config->num_tokens_after = 2;
+ config->include_inside_bag = true;
+ config->include_inside_length = true;
+ FeatureProcessorOptionsT options;
+ options.bounds_sensitive_features = std::move(config);
+ options.feature_version = 2;
+ flatbuffers::FlatBufferBuilder builder;
+ builder.Finish(CreateFeatureProcessorOptions(builder, &options));
+ flatbuffers::DetachedBuffer options_fb = builder.Release();
+
+ std::unique_ptr<std::vector<float>> features = MakeFeatures(9);
+ std::unique_ptr<std::vector<float>> padding_features(
+ new std::vector<float>{112233.0, -112233.0, 321.0});
+
+ const std::unique_ptr<CachedFeatures> cached_features =
+ CachedFeatures::Create(
+ {3, 9}, std::move(features), std::move(padding_features),
+ flatbuffers::GetRoot<FeatureProcessorOptions>(options_fb.data()),
+ /*feature_vector_size=*/3);
+ ASSERT_TRUE(cached_features);
+
+ EXPECT_THAT(
+ GetCachedBoundsSensitiveFeatures(*cached_features, {5, 8}),
+ ElementsAreFloat({11.0, -11.0, 0.1, 22.0, -22.0, 0.2, 33.0,
+ -33.0, 0.3, 44.0, -44.0, 0.4, 44.0, -44.0,
+ 0.4, 55.0, -55.0, 0.5, 66.0, -66.0, 0.6,
+ 112233.0, -112233.0, 321.0, 44.0, -44.0, 0.4, 3.0}));
+
+ EXPECT_THAT(
+ GetCachedBoundsSensitiveFeatures(*cached_features, {5, 7}),
+ ElementsAreFloat({11.0, -11.0, 0.1, 22.0, -22.0, 0.2, 33.0,
+ -33.0, 0.3, 44.0, -44.0, 0.4, 33.0, -33.0,
+ 0.3, 44.0, -44.0, 0.4, 55.0, -55.0, 0.5,
+ 66.0, -66.0, 0.6, 38.5, -38.5, 0.35, 2.0}));
+
+ EXPECT_THAT(
+ GetCachedBoundsSensitiveFeatures(*cached_features, {6, 8}),
+ ElementsAreFloat({22.0, -22.0, 0.2, 33.0, -33.0, 0.3, 44.0,
+ -44.0, 0.4, 55.0, -55.0, 0.5, 44.0, -44.0,
+ 0.4, 55.0, -55.0, 0.5, 66.0, -66.0, 0.6,
+ 112233.0, -112233.0, 321.0, 49.5, -49.5, 0.45, 2.0}));
+
+ EXPECT_THAT(
+ GetCachedBoundsSensitiveFeatures(*cached_features, {6, 7}),
+ ElementsAreFloat({22.0, -22.0, 0.2, 33.0, -33.0, 0.3,
+ 44.0, -44.0, 0.4, 112233.0, -112233.0, 321.0,
+ 112233.0, -112233.0, 321.0, 44.0, -44.0, 0.4,
+ 55.0, -55.0, 0.5, 66.0, -66.0, 0.6,
+ 44.0, -44.0, 0.4, 1.0}));
+}
+
+} // namespace
+} // namespace libtextclassifier2
diff --git a/common/algorithm.h b/common/algorithm.h
deleted file mode 100644
index 365eec9..0000000
--- a/common/algorithm.h
+++ /dev/null
@@ -1,49 +0,0 @@
-/*
- * Copyright (C) 2017 The Android Open Source Project
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-// Generic utils similar to those from the C++ header <algorithm>.
-
-#ifndef LIBTEXTCLASSIFIER_COMMON_ALGORITHM_H_
-#define LIBTEXTCLASSIFIER_COMMON_ALGORITHM_H_
-
-#include <algorithm>
-#include <vector>
-
-namespace libtextclassifier {
-namespace nlp_core {
-
-// Returns index of max element from the vector |elements|. Returns 0 if
-// |elements| is empty. T should be a type that can be compared by operator<.
-template<typename T>
-inline int GetArgMax(const std::vector<T> &elements) {
- return std::distance(
- elements.begin(),
- std::max_element(elements.begin(), elements.end()));
-}
-
-// Returns index of min element from the vector |elements|. Returns 0 if
-// |elements| is empty. T should be a type that can be compared by operator<.
-template<typename T>
-inline int GetArgMin(const std::vector<T> &elements) {
- return std::distance(
- elements.begin(),
- std::min_element(elements.begin(), elements.end()));
-}
-
-} // namespace nlp_core
-} // namespace libtextclassifier
-
-#endif // LIBTEXTCLASSIFIER_COMMON_ALGORITHM_H_
diff --git a/common/embedding-feature-extractor.cc b/common/embedding-feature-extractor.cc
deleted file mode 100644
index 254af45..0000000
--- a/common/embedding-feature-extractor.cc
+++ /dev/null
@@ -1,68 +0,0 @@
-/*
- * Copyright (C) 2017 The Android Open Source Project
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#include "common/embedding-feature-extractor.h"
-
-#include <stddef.h>
-
-#include <vector>
-
-#include "common/feature-extractor.h"
-#include "common/feature-types.h"
-#include "common/task-context.h"
-#include "util/base/integral_types.h"
-#include "util/base/logging.h"
-#include "util/strings/numbers.h"
-#include "util/strings/split.h"
-
-namespace libtextclassifier {
-namespace nlp_core {
-
-bool GenericEmbeddingFeatureExtractor::Init(TaskContext *context) {
- // Don't use version to determine how to get feature FML.
- const std::string features = context->Get(GetParamName("features"), "");
- TC_LOG(INFO) << "Features: " << features;
-
- const std::string embedding_names =
- context->Get(GetParamName("embedding_names"), "");
- TC_LOG(INFO) << "Embedding names: " << embedding_names;
-
- const std::string embedding_dims =
- context->Get(GetParamName("embedding_dims"), "");
- TC_LOG(INFO) << "Embedding dims: " << embedding_dims;
-
- embedding_fml_ = strings::Split(features, ';');
- embedding_names_ = strings::Split(embedding_names, ';');
- for (const std::string &dim : strings::Split(embedding_dims, ';')) {
- int32 parsed_dim = 0;
- if (!ParseInt32(dim.c_str(), &parsed_dim)) {
- TC_LOG(ERROR) << "Unable to parse dim " << dim;
- return false;
- }
- embedding_dims_.push_back(parsed_dim);
- }
- if ((embedding_fml_.size() != embedding_names_.size()) ||
- (embedding_fml_.size() != embedding_dims_.size())) {
- TC_LOG(ERROR) << "Mismatch: #fml specs = " << embedding_fml_.size()
- << "; #names = " << embedding_names_.size()
- << "; #dims = " << embedding_dims_.size();
- return false;
- }
- return true;
-}
-
-} // namespace nlp_core
-} // namespace libtextclassifier
diff --git a/common/embedding-feature-extractor.h b/common/embedding-feature-extractor.h
deleted file mode 100644
index 0efd0d2..0000000
--- a/common/embedding-feature-extractor.h
+++ /dev/null
@@ -1,197 +0,0 @@
-/*
- * Copyright (C) 2017 The Android Open Source Project
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#ifndef LIBTEXTCLASSIFIER_COMMON_EMBEDDING_FEATURE_EXTRACTOR_H_
-#define LIBTEXTCLASSIFIER_COMMON_EMBEDDING_FEATURE_EXTRACTOR_H_
-
-#include <memory>
-#include <string>
-#include <vector>
-
-#include "common/feature-extractor.h"
-#include "common/task-context.h"
-#include "common/workspace.h"
-#include "util/base/logging.h"
-#include "util/base/macros.h"
-
-namespace libtextclassifier {
-namespace nlp_core {
-
-// An EmbeddingFeatureExtractor manages the extraction of features for
-// embedding-based models. It wraps a sequence of underlying classes of feature
-// extractors, along with associated predicate maps. Each class of feature
-// extractors is associated with a name, e.g., "words", "labels", "tags".
-//
-// The class is split between a generic abstract version,
-// GenericEmbeddingFeatureExtractor (that can be initialized without knowing the
-// signature of the ExtractFeatures method) and a typed version.
-//
-// The predicate maps must be initialized before use: they can be loaded using
-// Read() or updated via UpdateMapsForExample.
-class GenericEmbeddingFeatureExtractor {
- public:
- GenericEmbeddingFeatureExtractor() {}
- virtual ~GenericEmbeddingFeatureExtractor() {}
-
- // Get the prefix std::string to put in front of all arguments, so they don't
- // conflict with other embedding models.
- virtual const std::string ArgPrefix() const = 0;
-
- // Initializes predicate maps and embedding space names that are common for
- // all embedding-based feature extractors.
- virtual bool Init(TaskContext *context);
-
- // Requests workspace for the underlying feature extractors. This is
- // implemented in the typed class.
- virtual void RequestWorkspaces(WorkspaceRegistry *registry) = 0;
-
- // Returns number of embedding spaces.
- int NumEmbeddings() const { return embedding_dims_.size(); }
-
- // Number of predicates for the embedding at a given index (vocabulary size).
- // Returns -1 if index is out of bounds.
- int EmbeddingSize(int index) const {
- const GenericFeatureExtractor *extractor = generic_feature_extractor(index);
- return (extractor == nullptr) ? -1 : extractor->GetDomainSize();
- }
-
- // Returns the dimensionality of the embedding space.
- int EmbeddingDims(int index) const { return embedding_dims_[index]; }
-
- // Accessor for embedding dims (dimensions of the embedding spaces).
- const std::vector<int> &embedding_dims() const { return embedding_dims_; }
-
- const std::vector<std::string> &embedding_fml() const {
- return embedding_fml_;
- }
-
- // Get parameter name by concatenating the prefix and the original name.
- std::string GetParamName(const std::string ¶m_name) const {
- std::string full_name = ArgPrefix();
- full_name.push_back('_');
- full_name.append(param_name);
- return full_name;
- }
-
- protected:
- // Provides the generic class with access to the templated extractors. This is
- // used to get the type information out of the feature extractor without
- // knowing the specific calling arguments of the extractor itself.
- // Returns nullptr for an out-of-bounds idx.
- virtual const GenericFeatureExtractor *generic_feature_extractor(
- int idx) const = 0;
-
- private:
- // Embedding space names for parameter sharing.
- std::vector<std::string> embedding_names_;
-
- // FML strings for each feature extractor.
- std::vector<std::string> embedding_fml_;
-
- // Size of each of the embedding spaces (maximum predicate id).
- std::vector<int> embedding_sizes_;
-
- // Embedding dimensions of the embedding spaces (i.e. 32, 64 etc.)
- std::vector<int> embedding_dims_;
-
- TC_DISALLOW_COPY_AND_ASSIGN(GenericEmbeddingFeatureExtractor);
-};
-
-// Templated, object-specific implementation of the
-// EmbeddingFeatureExtractor. EXTRACTOR should be a FeatureExtractor<OBJ,
-// ARGS...> class that has the appropriate FeatureTraits() to ensure that
-// locator type features work.
-//
-// Note: for backwards compatibility purposes, this always reads the FML spec
-// from "<prefix>_features".
-template <class EXTRACTOR, class OBJ, class... ARGS>
-class EmbeddingFeatureExtractor : public GenericEmbeddingFeatureExtractor {
- public:
- // Initializes all predicate maps, feature extractors, etc.
- bool Init(TaskContext *context) override {
- if (!GenericEmbeddingFeatureExtractor::Init(context)) {
- return false;
- }
- feature_extractors_.resize(embedding_fml().size());
- for (int i = 0; i < embedding_fml().size(); ++i) {
- feature_extractors_[i].reset(new EXTRACTOR());
- if (!feature_extractors_[i]->Parse(embedding_fml()[i])) {
- return false;
- }
- if (!feature_extractors_[i]->Setup(context)) {
- return false;
- }
- }
- for (auto &feature_extractor : feature_extractors_) {
- if (!feature_extractor->Init(context)) {
- return false;
- }
- }
- return true;
- }
-
- // Requests workspaces from the registry. Must be called after Init(), and
- // before Preprocess().
- void RequestWorkspaces(WorkspaceRegistry *registry) override {
- for (auto &feature_extractor : feature_extractors_) {
- feature_extractor->RequestWorkspaces(registry);
- }
- }
-
- // Must be called on the object one state for each sentence, before any
- // feature extraction (e.g., UpdateMapsForExample, ExtractFeatures).
- void Preprocess(WorkspaceSet *workspaces, OBJ *obj) const {
- for (auto &feature_extractor : feature_extractors_) {
- feature_extractor->Preprocess(workspaces, obj);
- }
- }
-
- // Extracts features using the extractors. Note that features must already
- // be initialized to the correct number of feature extractors. No predicate
- // mapping is applied.
- void ExtractFeatures(const WorkspaceSet &workspaces, const OBJ &obj,
- ARGS... args,
- std::vector<FeatureVector> *features) const {
- TC_DCHECK(features != nullptr);
- TC_DCHECK_EQ(features->size(), feature_extractors_.size());
- for (int i = 0; i < feature_extractors_.size(); ++i) {
- (*features)[i].clear();
- feature_extractors_[i]->ExtractFeatures(workspaces, obj, args...,
- &(*features)[i]);
- }
- }
-
- protected:
- // Provides generic access to the feature extractors.
- const GenericFeatureExtractor *generic_feature_extractor(
- int idx) const override {
- if ((idx < 0) || (idx >= feature_extractors_.size())) {
- TC_LOG(ERROR) << "Out of bounds index " << idx;
- TC_DCHECK(false); // Crash in debug mode.
- return nullptr;
- }
- return feature_extractors_[idx].get();
- }
-
- private:
- // Templated feature extractor class.
- std::vector<std::unique_ptr<EXTRACTOR>> feature_extractors_;
-};
-
-} // namespace nlp_core
-} // namespace libtextclassifier
-
-#endif // LIBTEXTCLASSIFIER_COMMON_EMBEDDING_FEATURE_EXTRACTOR_H_
diff --git a/common/embedding-feature-extractor_test.cc b/common/embedding-feature-extractor_test.cc
deleted file mode 100644
index c5ed627..0000000
--- a/common/embedding-feature-extractor_test.cc
+++ /dev/null
@@ -1,142 +0,0 @@
-/*
- * Copyright (C) 2017 The Android Open Source Project
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#include "common/embedding-feature-extractor.h"
-
-#include "lang_id/language-identifier-features.h"
-#include "lang_id/light-sentence-features.h"
-#include "lang_id/light-sentence.h"
-#include "lang_id/relevant-script-feature.h"
-#include "gtest/gtest.h"
-
-namespace libtextclassifier {
-namespace nlp_core {
-
-class EmbeddingFeatureExtractorTest : public ::testing::Test {
- public:
- void SetUp() override {
- // Make sure all relevant features are registered:
- lang_id::ContinuousBagOfNgramsFunction::RegisterClass();
- lang_id::RelevantScriptFeature::RegisterClass();
- }
-};
-
-// Specialization of EmbeddingFeatureExtractor that extracts from LightSentence.
-class TestEmbeddingFeatureExtractor
- : public EmbeddingFeatureExtractor<lang_id::LightSentenceExtractor,
- lang_id::LightSentence> {
- public:
- const std::string ArgPrefix() const override { return "test"; }
-};
-
-TEST_F(EmbeddingFeatureExtractorTest, NoEmbeddingSpaces) {
- TaskContext context;
- context.SetParameter("test_features", "");
- context.SetParameter("test_embedding_names", "");
- context.SetParameter("test_embedding_dims", "");
- TestEmbeddingFeatureExtractor tefe;
- ASSERT_TRUE(tefe.Init(&context));
- EXPECT_EQ(tefe.NumEmbeddings(), 0);
-}
-
-TEST_F(EmbeddingFeatureExtractorTest, GoodSpec) {
- TaskContext context;
- const std::string spec =
- "continuous-bag-of-ngrams(id_dim=5000,size=3);"
- "continuous-bag-of-ngrams(id_dim=7000,size=4)";
- context.SetParameter("test_features", spec);
- context.SetParameter("test_embedding_names", "trigram;quadgram");
- context.SetParameter("test_embedding_dims", "16;24");
- TestEmbeddingFeatureExtractor tefe;
- ASSERT_TRUE(tefe.Init(&context));
- EXPECT_EQ(tefe.NumEmbeddings(), 2);
- EXPECT_EQ(tefe.EmbeddingSize(0), 5000);
- EXPECT_EQ(tefe.EmbeddingDims(0), 16);
- EXPECT_EQ(tefe.EmbeddingSize(1), 7000);
- EXPECT_EQ(tefe.EmbeddingDims(1), 24);
-}
-
-TEST_F(EmbeddingFeatureExtractorTest, MissmatchFmlVsNames) {
- TaskContext context;
- const std::string spec =
- "continuous-bag-of-ngrams(id_dim=5000,size=3);"
- "continuous-bag-of-ngrams(id_dim=7000,size=4)";
- context.SetParameter("test_features", spec);
- context.SetParameter("test_embedding_names", "trigram");
- context.SetParameter("test_embedding_dims", "16;16");
- TestEmbeddingFeatureExtractor tefe;
- ASSERT_FALSE(tefe.Init(&context));
-}
-
-TEST_F(EmbeddingFeatureExtractorTest, MissmatchFmlVsDims) {
- TaskContext context;
- const std::string spec =
- "continuous-bag-of-ngrams(id_dim=5000,size=3);"
- "continuous-bag-of-ngrams(id_dim=7000,size=4)";
- context.SetParameter("test_features", spec);
- context.SetParameter("test_embedding_names", "trigram;quadgram");
- context.SetParameter("test_embedding_dims", "16;16;32");
- TestEmbeddingFeatureExtractor tefe;
- ASSERT_FALSE(tefe.Init(&context));
-}
-
-TEST_F(EmbeddingFeatureExtractorTest, BrokenSpec) {
- TaskContext context;
- const std::string spec =
- "continuous-bag-of-ngrams(id_dim=5000;"
- "continuous-bag-of-ngrams(id_dim=7000,size=4)";
- context.SetParameter("test_features", spec);
- context.SetParameter("test_embedding_names", "trigram;quadgram");
- context.SetParameter("test_embedding_dims", "16;16");
- TestEmbeddingFeatureExtractor tefe;
- ASSERT_FALSE(tefe.Init(&context));
-}
-
-TEST_F(EmbeddingFeatureExtractorTest, MissingFeature) {
- TaskContext context;
- const std::string spec =
- "continuous-bag-of-ngrams(id_dim=5000,size=3);"
- "no-such-feature";
- context.SetParameter("test_features", spec);
- context.SetParameter("test_embedding_names", "trigram;foo");
- context.SetParameter("test_embedding_dims", "16;16");
- TestEmbeddingFeatureExtractor tefe;
- ASSERT_FALSE(tefe.Init(&context));
-}
-
-TEST_F(EmbeddingFeatureExtractorTest, MultipleFeatures) {
- TaskContext context;
- const std::string spec =
- "continuous-bag-of-ngrams(id_dim=1000,size=3);"
- "continuous-bag-of-relevant-scripts";
- context.SetParameter("test_features", spec);
- context.SetParameter("test_embedding_names", "trigram;script");
- context.SetParameter("test_embedding_dims", "8;16");
- TestEmbeddingFeatureExtractor tefe;
- ASSERT_TRUE(tefe.Init(&context));
- EXPECT_EQ(tefe.NumEmbeddings(), 2);
- EXPECT_EQ(tefe.EmbeddingSize(0), 1000);
- EXPECT_EQ(tefe.EmbeddingDims(0), 8);
-
- // continuous-bag-of-relevant-scripts has its own hard-wired vocabulary size.
- // We don't want this test to depend on that value; we just check it's bigger
- // than 0.
- EXPECT_GT(tefe.EmbeddingSize(1), 0);
- EXPECT_EQ(tefe.EmbeddingDims(1), 16);
-}
-
-} // namespace nlp_core
-} // namespace libtextclassifier
diff --git a/common/embedding-network-package.proto b/common/embedding-network-package.proto
deleted file mode 100644
index 54d47e6..0000000
--- a/common/embedding-network-package.proto
+++ /dev/null
@@ -1,29 +0,0 @@
-// Copyright (C) 2017 The Android Open Source Project
-//
-// Licensed under the Apache License, Version 2.0 (the "License");
-// you may not use this file except in compliance with the License.
-// You may obtain a copy of the License at
-//
-// http://www.apache.org/licenses/LICENSE-2.0
-//
-// Unless required by applicable law or agreed to in writing, software
-// distributed under the License is distributed on an "AS IS" BASIS,
-// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-// See the License for the specific language governing permissions and
-// limitations under the License.
-
-// This file defines TaskSpec as an extension to EmbeddingNetworkProto. The
-// definition is done here rather than directly in the imported protos to keep
-// the different messages as independent as possible.
-
-syntax = "proto2";
-option optimize_for = LITE_RUNTIME;
-
-import "external/libtextclassifier/common/task-spec.proto";
-import "external/libtextclassifier/common/embedding-network.proto";
-
-package libtextclassifier.nlp_core;
-
-extend EmbeddingNetworkProto {
- optional TaskSpec task_spec_in_embedding_network_proto = 129692954;
-}
diff --git a/common/embedding-network-params-from-proto.h b/common/embedding-network-params-from-proto.h
deleted file mode 100644
index 2f2c429..0000000
--- a/common/embedding-network-params-from-proto.h
+++ /dev/null
@@ -1,245 +0,0 @@
-/*
- * Copyright (C) 2017 The Android Open Source Project
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#ifndef LIBTEXTCLASSIFIER_COMMON_EMBEDDING_NETWORK_PARAMS_FROM_PROTO_H_
-#define LIBTEXTCLASSIFIER_COMMON_EMBEDDING_NETWORK_PARAMS_FROM_PROTO_H_
-
-#include <algorithm>
-#include <memory>
-#include <string>
-#include <utility>
-#include <vector>
-
-#include "common/embedding-network-package.pb.h"
-#include "common/embedding-network-params.h"
-#include "common/embedding-network.pb.h"
-#include "common/float16.h"
-#include "common/little-endian-data.h"
-#include "common/task-context.h"
-#include "common/task-spec.pb.h"
-#include "util/base/integral_types.h"
-#include "util/base/logging.h"
-
-namespace libtextclassifier {
-namespace nlp_core {
-
-// A wrapper class that owns and exposes an EmbeddingNetworkProto message via
-// the EmbeddingNetworkParams interface.
-//
-// The EmbeddingNetworkParams interface encapsulates the weight matrices of the
-// embeddings, hidden and softmax layers as transposed versions of their
-// counterparts in the original EmbeddingNetworkProto. The matrices in the proto
-// passed to this class' constructor must likewise already have been transposed.
-// See embedding-network-params.h for details.
-class EmbeddingNetworkParamsFromProto : public EmbeddingNetworkParams {
- public:
- // Constructor that takes ownership of the provided proto. See class-comment
- // for the requirements that certain weight matrices must satisfy.
- explicit EmbeddingNetworkParamsFromProto(
- std::unique_ptr<EmbeddingNetworkProto> proto)
- : proto_(std::move(proto)) {
- valid_ = true;
-
- // Initialize these vectors to have the required number of elements
- // regardless of quantization status. This is to support the unlikely case
- // where only some embeddings are quantized, along with the fact that
- // EmbeddingNetworkParams interface accesses them by index.
- embeddings_quant_scales_.resize(proto_->embeddings_size());
- embeddings_quant_weights_.resize(proto_->embeddings_size());
- for (int i = 0; i < proto_->embeddings_size(); ++i) {
- MatrixParams *embedding = proto_->mutable_embeddings()->Mutable(i);
- if (!embedding->is_quantized()) {
- continue;
- }
-
- bool success = FillVectorFromDataBytesInLittleEndian(
- embedding->bytes_for_quantized_values(),
- embedding->rows() * embedding->cols(),
- &(embeddings_quant_weights_[i]));
- if (!success) {
- TC_LOG(ERROR) << "Problem decoding quant_weights for embeddings #" << i;
- valid_ = false;
- }
-
- // The repeated field bytes_for_quantized_values uses a lot of memory.
- // Since it's no longer necessary (and we own the proto), we clear it.
- embedding->clear_bytes_for_quantized_values();
-
- success = FillVectorFromDataBytesInLittleEndian(
- embedding->bytes_for_col_scales(),
- embedding->rows(),
- &(embeddings_quant_scales_[i]));
- if (!success) {
- TC_LOG(ERROR) << "Problem decoding col_scales for embeddings #" << i;
- valid_ = false;
- }
-
- // See comments for clear_bytes_for_quantized_values().
- embedding->clear_bytes_for_col_scales();
- }
- }
-
- const TaskSpec *GetTaskSpec() override {
- if (!proto_) {
- return nullptr;
- }
- auto extension_id = task_spec_in_embedding_network_proto;
- if (proto_->HasExtension(extension_id)) {
- return &(proto_->GetExtension(extension_id));
- } else {
- TC_LOG(ERROR) << "Unable to get TaskSpec from EmbeddingNetworkProto";
- return nullptr;
- }
- }
-
- // Returns true if these params are valid. False otherwise (e.g., if the
- // original proto data was corrupted).
- bool is_valid() { return valid_; }
-
- protected:
- int embeddings_size() const override { return proto_->embeddings_size(); }
-
- int embeddings_num_rows(int i) const override {
- TC_DCHECK(InRange(i, embeddings_size()));
- return proto_->embeddings(i).rows();
- }
-
- int embeddings_num_cols(int i) const override {
- TC_DCHECK(InRange(i, embeddings_size()));
- return proto_->embeddings(i).cols();
- }
-
- const void *embeddings_weights(int i) const override {
- TC_DCHECK(InRange(i, embeddings_size()));
- if (proto_->embeddings(i).is_quantized()) {
- return static_cast<const void *>(embeddings_quant_weights_.at(i).data());
- } else {
- return static_cast<const void *>(proto_->embeddings(i).value().data());
- }
- }
-
- QuantizationType embeddings_quant_type(int i) const override {
- TC_DCHECK(InRange(i, embeddings_size()));
- return proto_->embeddings(i).is_quantized() ? QuantizationType::UINT8
- : QuantizationType::NONE;
- }
-
- const float16 *embeddings_quant_scales(int i) const override {
- TC_DCHECK(InRange(i, embeddings_size()));
- return proto_->embeddings(i).is_quantized()
- ? embeddings_quant_scales_.at(i).data()
- : nullptr;
- }
-
- int hidden_size() const override { return proto_->hidden_size(); }
-
- int hidden_num_rows(int i) const override {
- TC_DCHECK(InRange(i, hidden_size()));
- return proto_->hidden(i).rows();
- }
-
- int hidden_num_cols(int i) const override {
- TC_DCHECK(InRange(i, hidden_size()));
- return proto_->hidden(i).cols();
- }
-
- const void *hidden_weights(int i) const override {
- TC_DCHECK(InRange(i, hidden_size()));
- return proto_->hidden(i).value().data();
- }
-
- int hidden_bias_size() const override { return proto_->hidden_bias_size(); }
-
- int hidden_bias_num_rows(int i) const override {
- TC_DCHECK(InRange(i, hidden_bias_size()));
- return proto_->hidden_bias(i).rows();
- }
-
- int hidden_bias_num_cols(int i) const override {
- TC_DCHECK(InRange(i, hidden_bias_size()));
- return proto_->hidden_bias(i).cols();
- }
-
- const void *hidden_bias_weights(int i) const override {
- TC_DCHECK(InRange(i, hidden_bias_size()));
- return proto_->hidden_bias(i).value().data();
- }
-
- int softmax_size() const override { return proto_->has_softmax() ? 1 : 0; }
-
- int softmax_num_rows(int i) const override {
- TC_DCHECK(InRange(i, softmax_size()));
- return proto_->has_softmax() ? proto_->softmax().rows() : 0;
- }
-
- int softmax_num_cols(int i) const override {
- TC_DCHECK(InRange(i, softmax_size()));
- return proto_->has_softmax() ? proto_->softmax().cols() : 0;
- }
-
- const void *softmax_weights(int i) const override {
- TC_DCHECK(InRange(i, softmax_size()));
- return proto_->has_softmax() ? proto_->softmax().value().data() : nullptr;
- }
-
- int softmax_bias_size() const override {
- return proto_->has_softmax_bias() ? 1 : 0;
- }
-
- int softmax_bias_num_rows(int i) const override {
- TC_DCHECK(InRange(i, softmax_bias_size()));
- return proto_->has_softmax_bias() ? proto_->softmax_bias().rows() : 0;
- }
-
- int softmax_bias_num_cols(int i) const override {
- TC_DCHECK(InRange(i, softmax_bias_size()));
- return proto_->has_softmax_bias() ? proto_->softmax_bias().cols() : 0;
- }
-
- const void *softmax_bias_weights(int i) const override {
- TC_DCHECK(InRange(i, softmax_bias_size()));
- return proto_->has_softmax_bias() ? proto_->softmax_bias().value().data()
- : nullptr;
- }
-
- int embedding_num_features_size() const override {
- return proto_->embedding_num_features_size();
- }
-
- int embedding_num_features(int i) const override {
- TC_DCHECK(InRange(i, embedding_num_features_size()));
- return proto_->embedding_num_features(i);
- }
-
- private:
- std::unique_ptr<EmbeddingNetworkProto> proto_;
-
- // True if these params are valid. May be false if the original proto was
- // corrupted. We prefer to set this to false to CHECK-failing.
- bool valid_;
-
- // When the embeddings are quantized, these members are used to store their
- // numeric values using the types expected by the rest of the class. Due to
- // technical reasons, the proto stores this info using larger types (i.e.,
- // more bits).
- std::vector<std::vector<float16>> embeddings_quant_scales_;
- std::vector<std::vector<uint8>> embeddings_quant_weights_;
-};
-
-} // namespace nlp_core
-} // namespace libtextclassifier
-
-#endif // LIBTEXTCLASSIFIER_COMMON_EMBEDDING_NETWORK_PARAMS_FROM_PROTO_H_
diff --git a/common/embedding-network-params.h b/common/embedding-network-params.h
deleted file mode 100755
index ee2d9dc..0000000
--- a/common/embedding-network-params.h
+++ /dev/null
@@ -1,325 +0,0 @@
-/*
- * Copyright (C) 2017 The Android Open Source Project
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#ifndef LIBTEXTCLASSIFIER_COMMON_EMBEDDING_NETWORK_PARAMS_H_
-#define LIBTEXTCLASSIFIER_COMMON_EMBEDDING_NETWORK_PARAMS_H_
-
-#include <algorithm>
-#include <string>
-
-#include "common/float16.h"
-#include "common/task-context.h"
-#include "common/task-spec.pb.h"
-#include "util/base/logging.h"
-
-namespace libtextclassifier {
-namespace nlp_core {
-
-enum class QuantizationType { NONE = 0, UINT8 };
-
-// API for accessing parameters for a feed-forward neural network with
-// embeddings.
-//
-// Note: this API is closely related to embedding-network.proto. The reason we
-// have a separate API is that the proto may not be the only way of packaging
-// these parameters.
-class EmbeddingNetworkParams {
- public:
- virtual ~EmbeddingNetworkParams() {}
-
- // **** High-level API.
-
- // Simple representation of a matrix. This small struct that doesn't own any
- // resource intentionally supports copy / assign, to simplify our APIs.
- struct Matrix {
- // Number of rows.
- int rows;
-
- // Number of columns.
- int cols;
-
- QuantizationType quant_type;
-
- // Pointer to matrix elements, in row-major order
- // (https://en.wikipedia.org/wiki/Row-major_order) Not owned.
- const void *elements;
-
- // Quantization scales: one scale for each row.
- const float16 *quant_scales;
- };
-
- // Returns number of embedding spaces.
- int GetNumEmbeddingSpaces() const {
- if (embeddings_size() != embedding_num_features_size()) {
- TC_LOG(ERROR) << "Embedding spaces mismatch " << embeddings_size()
- << " != " << embedding_num_features_size();
- }
- return std::max(0,
- std::min(embeddings_size(), embedding_num_features_size()));
- }
-
- // Returns embedding matrix for the i-th embedding space.
- //
- // NOTE: i must be in [0, GetNumEmbeddingSpaces()). Undefined behavior
- // otherwise.
- Matrix GetEmbeddingMatrix(int i) const {
- TC_DCHECK(InRange(i, embeddings_size()));
- Matrix matrix;
- matrix.rows = embeddings_num_rows(i);
- matrix.cols = embeddings_num_cols(i);
- matrix.elements = embeddings_weights(i);
- matrix.quant_type = embeddings_quant_type(i);
- matrix.quant_scales = embeddings_quant_scales(i);
- return matrix;
- }
-
- // Returns number of features in i-th embedding space.
- //
- // NOTE: i must be in [0, GetNumEmbeddingSpaces()). Undefined behavior
- // otherwise.
- int GetNumFeaturesInEmbeddingSpace(int i) const {
- TC_DCHECK(InRange(i, embedding_num_features_size()));
- return std::max(0, embedding_num_features(i));
- }
-
- // Returns number of hidden layers in the neural network. Each such layer has
- // weight matrix and a bias vector (a matrix with one column).
- int GetNumHiddenLayers() const {
- if (hidden_size() != hidden_bias_size()) {
- TC_LOG(ERROR) << "Hidden layer mismatch " << hidden_size()
- << " != " << hidden_bias_size();
- }
- return std::max(0, std::min(hidden_size(), hidden_bias_size()));
- }
-
- // Returns weight matrix for i-th hidden layer.
- //
- // NOTE: i must be in [0, GetNumHiddenLayers()). Undefined behavior
- // otherwise.
- Matrix GetHiddenLayerMatrix(int i) const {
- TC_DCHECK(InRange(i, hidden_size()));
- Matrix matrix;
- matrix.rows = hidden_num_rows(i);
- matrix.cols = hidden_num_cols(i);
-
- // Quantization not supported here.
- matrix.quant_type = QuantizationType::NONE;
- matrix.elements = hidden_weights(i);
- return matrix;
- }
-
- // Returns bias matrix for i-th hidden layer. Technically a Matrix, but we
- // expect it to be a vector (i.e., num cols is 1).
- //
- // NOTE: i must be in [0, GetNumHiddenLayers()). Undefined behavior
- // otherwise.
- Matrix GetHiddenLayerBias(int i) const {
- TC_DCHECK(InRange(i, hidden_bias_size()));
- Matrix matrix;
- matrix.rows = hidden_bias_num_rows(i);
- matrix.cols = hidden_bias_num_cols(i);
-
- // Quantization not supported here.
- matrix.quant_type = QuantizationType::NONE;
- matrix.elements = hidden_bias_weights(i);
- return matrix;
- }
-
- // Returns true if a softmax layer exists.
- bool HasSoftmaxLayer() const {
- if (softmax_size() != softmax_bias_size()) {
- TC_LOG(ERROR) << "Softmax layer mismatch " << softmax_size()
- << " != " << softmax_bias_size();
- }
- return (softmax_size() == 1) && (softmax_bias_size() == 1);
- }
-
- // Returns weight matrix for the softmax layer.
- //
- // NOTE: Should be called only if HasSoftmaxLayer() is true. Undefined
- // behavior otherwise.
- Matrix GetSoftmaxMatrix() const {
- TC_DCHECK(softmax_size() == 1);
- Matrix matrix;
- matrix.rows = softmax_num_rows(0);
- matrix.cols = softmax_num_cols(0);
-
- // Quantization not supported here.
- matrix.quant_type = QuantizationType::NONE;
- matrix.elements = softmax_weights(0);
- return matrix;
- }
-
- // Returns bias for the softmax layer. Technically a Matrix, but we expect it
- // to be a row/column vector (i.e., num cols is 1).
- //
- // NOTE: Should be called only if HasSoftmaxLayer() is true. Undefined
- // behavior otherwise.
- Matrix GetSoftmaxBias() const {
- TC_DCHECK(softmax_bias_size() == 1);
- Matrix matrix;
- matrix.rows = softmax_bias_num_rows(0);
- matrix.cols = softmax_bias_num_cols(0);
-
- // Quantization not supported here.
- matrix.quant_type = QuantizationType::NONE;
- matrix.elements = softmax_bias_weights(0);
- return matrix;
- }
-
- // Updates the EmbeddingNetwork-related parameters from task_context. Returns
- // true on success, false on error.
- virtual bool UpdateTaskContextParameters(TaskContext *task_context) {
- const TaskSpec *task_spec = GetTaskSpec();
- if (task_spec == nullptr) {
- TC_LOG(ERROR) << "Unable to get TaskSpec";
- return false;
- }
- for (const TaskSpec::Parameter ¶meter : task_spec->parameter()) {
- task_context->SetParameter(parameter.name(), parameter.value());
- }
- return true;
- }
-
- // Returns a pointer to a TaskSpec with the EmbeddingNetwork-related
- // parameters. Returns nullptr in case of problems. Ownership with the
- // returned pointer is *not* transfered to the caller.
- virtual const TaskSpec *GetTaskSpec() {
- TC_LOG(ERROR) << "Not implemented";
- return nullptr;
- }
-
- protected:
- // **** Low-level API.
- //
- // * Most low-level API methods are documented by giving an equivalent
- // function call on proto, the original proto (of type
- // EmbeddingNetworkProto) which was used to generate the C++ code.
- //
- // * To simplify our generation code, optional proto fields of message type
- // are treated as repeated fields with 0 or 1 instances. As such, we have
- // *_size() methods for such optional fields: they return 0 or 1.
- //
- // * "transpose(M)" denotes the transpose of a matrix M.
- //
- // * Behavior is undefined when trying to retrieve a piece of data that does
- // not exist: e.g., embeddings_num_rows(5) if embeddings_size() == 2.
-
- // ** Access methods for repeated MatrixParams embeddings.
- //
- // Returns proto.embeddings_size().
- virtual int embeddings_size() const = 0;
-
- // Returns number of rows of transpose(proto.embeddings(i)).
- virtual int embeddings_num_rows(int i) const = 0;
-
- // Returns number of columns of transpose(proto.embeddings(i)).
- virtual int embeddings_num_cols(int i) const = 0;
-
- // Returns pointer to elements of transpose(proto.embeddings(i)), in row-major
- // order. NOTE: for unquantized embeddings, this returns a pointer to float;
- // for quantized embeddings, this returns a pointer to uint8.
- virtual const void *embeddings_weights(int i) const = 0;
-
- virtual QuantizationType embeddings_quant_type(int i) const {
- return QuantizationType::NONE;
- }
-
- virtual const float16 *embeddings_quant_scales(int i) const {
- return nullptr;
- }
-
- // ** Access methods for repeated MatrixParams hidden.
- //
- // Returns embedding_network_proto.hidden_size().
- virtual int hidden_size() const = 0;
-
- // Returns embedding_network_proto.hidden(i).rows().
- virtual int hidden_num_rows(int i) const = 0;
-
- // Returns embedding_network_proto.hidden(i).rows().
- virtual int hidden_num_cols(int i) const = 0;
-
- // Returns pointer to beginning of array of floats with all values from
- // embedding_network_proto.hidden(i).
- virtual const void *hidden_weights(int i) const = 0;
-
- // ** Access methods for repeated MatrixParams hidden_bias.
- //
- // Returns proto.hidden_bias_size().
- virtual int hidden_bias_size() const = 0;
-
- // Returns number of rows of proto.hidden_bias(i).
- virtual int hidden_bias_num_rows(int i) const = 0;
-
- // Returns number of columns of proto.hidden_bias(i).
- virtual int hidden_bias_num_cols(int i) const = 0;
-
- // Returns pointer to elements of proto.hidden_bias(i), in row-major order.
- virtual const void *hidden_bias_weights(int i) const = 0;
-
- // ** Access methods for optional MatrixParams softmax.
- //
- // Returns 1 if proto has optional field softmax, 0 otherwise.
- virtual int softmax_size() const = 0;
-
- // Returns number of rows of transpose(proto.softmax()).
- virtual int softmax_num_rows(int i) const = 0;
-
- // Returns number of columns of transpose(proto.softmax()).
- virtual int softmax_num_cols(int i) const = 0;
-
- // Returns pointer to elements of transpose(proto.softmax()), in row-major
- // order.
- virtual const void *softmax_weights(int i) const = 0;
-
- // ** Access methods for optional MatrixParams softmax_bias.
- //
- // Returns 1 if proto has optional field softmax_bias, 0 otherwise.
- virtual int softmax_bias_size() const = 0;
-
- // Returns number of rows of proto.softmax_bias().
- virtual int softmax_bias_num_rows(int i) const = 0;
-
- // Returns number of columns of proto.softmax_bias().
- virtual int softmax_bias_num_cols(int i) const = 0;
-
- // Returns pointer to elements of proto.softmax_bias(), in row-major order.
- virtual const void *softmax_bias_weights(int i) const = 0;
-
- // ** Access methods for repeated int32 embedding_num_features.
- //
- // Returns proto.embedding_num_features_size().
- virtual int embedding_num_features_size() const = 0;
-
- // Returns proto.embedding_num_features(i).
- virtual int embedding_num_features(int i) const = 0;
-
- // Returns true if and only if index is in range [0, size). Log an error
- // message otherwise.
- static bool InRange(int index, int size) {
- if ((index < 0) || (index >= size)) {
- TC_LOG(ERROR) << "Index " << index << " outside [0, " << size << ")";
- return false;
- }
- return true;
- }
-}; // class EmbeddingNetworkParams
-
-} // namespace nlp_core
-} // namespace libtextclassifier
-
-#endif // LIBTEXTCLASSIFIER_COMMON_EMBEDDING_NETWORK_PARAMS_H_
diff --git a/common/embedding-network.cc b/common/embedding-network.cc
deleted file mode 100644
index b27cda3..0000000
--- a/common/embedding-network.cc
+++ /dev/null
@@ -1,380 +0,0 @@
-/*
- * Copyright (C) 2017 The Android Open Source Project
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#include "common/embedding-network.h"
-
-#include <math.h>
-
-#include "common/simple-adder.h"
-#include "util/base/integral_types.h"
-#include "util/base/logging.h"
-
-namespace libtextclassifier {
-namespace nlp_core {
-
-namespace {
-
-// Returns true if and only if matrix does not use any quantization.
-bool CheckNoQuantization(const EmbeddingNetworkParams::Matrix &matrix) {
- if (matrix.quant_type != QuantizationType::NONE) {
- TC_LOG(ERROR) << "Unsupported quantization";
- TC_DCHECK(false); // Crash in debug mode.
- return false;
- }
- return true;
-}
-
-// Initializes a Matrix object with the parameters from the MatrixParams
-// source_matrix. source_matrix should not use quantization.
-//
-// Returns true on success, false on error.
-bool InitNonQuantizedMatrix(const EmbeddingNetworkParams::Matrix &source_matrix,
- EmbeddingNetwork::Matrix *mat) {
- mat->resize(source_matrix.rows);
-
- // Before we access the weights as floats, we need to check that they are
- // really floats, i.e., no quantization is used.
- if (!CheckNoQuantization(source_matrix)) return false;
- const float *weights =
- reinterpret_cast<const float *>(source_matrix.elements);
- for (int r = 0; r < source_matrix.rows; ++r) {
- (*mat)[r] = EmbeddingNetwork::VectorWrapper(weights, source_matrix.cols);
- weights += source_matrix.cols;
- }
- return true;
-}
-
-// Initializes a VectorWrapper object with the parameters from the MatrixParams
-// source_matrix. source_matrix should have exactly one column and should not
-// use quantization.
-//
-// Returns true on success, false on error.
-bool InitNonQuantizedVector(const EmbeddingNetworkParams::Matrix &source_matrix,
- EmbeddingNetwork::VectorWrapper *vector) {
- if (source_matrix.cols != 1) {
- TC_LOG(ERROR) << "wrong #cols " << source_matrix.cols;
- return false;
- }
- if (!CheckNoQuantization(source_matrix)) {
- TC_LOG(ERROR) << "unsupported quantization";
- return false;
- }
- // Before we access the weights as floats, we need to check that they are
- // really floats, i.e., no quantization is used.
- if (!CheckNoQuantization(source_matrix)) return false;
- const float *weights =
- reinterpret_cast<const float *>(source_matrix.elements);
- *vector = EmbeddingNetwork::VectorWrapper(weights, source_matrix.rows);
- return true;
-}
-
-// Computes y = weights * Relu(x) + b where Relu is optionally applied.
-template <typename ScaleAdderClass>
-bool SparseReluProductPlusBias(bool apply_relu,
- const EmbeddingNetwork::Matrix &weights,
- const EmbeddingNetwork::VectorWrapper &b,
- const VectorSpan<float> &x,
- EmbeddingNetwork::Vector *y) {
- // Check that dimensions match.
- if ((x.size() != weights.size()) || weights.empty()) {
- TC_LOG(ERROR) << x.size() << " != " << weights.size();
- return false;
- }
- if (weights[0].size() != b.size()) {
- TC_LOG(ERROR) << weights[0].size() << " != " << b.size();
- return false;
- }
-
- y->assign(b.data(), b.data() + b.size());
- ScaleAdderClass adder(y->data(), y->size());
-
- const int x_size = x.size();
- for (int i = 0; i < x_size; ++i) {
- const float &scale = x[i];
- if (apply_relu) {
- if (scale > 0) {
- adder.LazyScaleAdd(weights[i].data(), scale);
- }
- } else {
- adder.LazyScaleAdd(weights[i].data(), scale);
- }
- }
- return true;
-}
-} // namespace
-
-bool EmbeddingNetwork::ConcatEmbeddings(
- const std::vector<FeatureVector> &feature_vectors, Vector *concat) const {
- concat->resize(concat_layer_size_);
-
- // Invariant 1: feature_vectors contains exactly one element for each
- // embedding space. That element is itself a FeatureVector, which may be
- // empty, but it should be there.
- if (feature_vectors.size() != embedding_matrices_.size()) {
- TC_LOG(ERROR) << feature_vectors.size()
- << " != " << embedding_matrices_.size();
- return false;
- }
-
- // "es_index" stands for "embedding space index".
- for (int es_index = 0; es_index < feature_vectors.size(); ++es_index) {
- // Access is safe by es_index loop bounds and Invariant 1.
- EmbeddingMatrix *const embedding_matrix =
- embedding_matrices_[es_index].get();
- if (embedding_matrix == nullptr) {
- // Should not happen, hence our terse log error message.
- TC_LOG(ERROR) << es_index;
- return false;
- }
-
- // Access is safe due to es_index loop bounds.
- const FeatureVector &feature_vector = feature_vectors[es_index];
-
- // Access is safe by es_index loop bounds, Invariant 1, and Invariant 2.
- const int concat_offset = concat_offset_[es_index];
-
- if (!GetEmbeddingInternal(feature_vector, embedding_matrix, concat_offset,
- concat->data(), concat->size())) {
- TC_LOG(ERROR) << es_index;
- return false;
- }
- }
- return true;
-}
-
-bool EmbeddingNetwork::GetEmbedding(const FeatureVector &feature_vector,
- int es_index, float *embedding) const {
- EmbeddingMatrix *const embedding_matrix = embedding_matrices_[es_index].get();
- if (embedding_matrix == nullptr) {
- // Should not happen, hence our terse log error message.
- TC_LOG(ERROR) << es_index;
- return false;
- }
- return GetEmbeddingInternal(feature_vector, embedding_matrix, 0, embedding,
- embedding_matrices_[es_index]->dim());
-}
-
-bool EmbeddingNetwork::GetEmbeddingInternal(
- const FeatureVector &feature_vector,
- EmbeddingMatrix *const embedding_matrix, const int concat_offset,
- float *concat, int concat_size) const {
- const int embedding_dim = embedding_matrix->dim();
- const bool is_quantized =
- embedding_matrix->quant_type() != QuantizationType::NONE;
- const int num_features = feature_vector.size();
- for (int fi = 0; fi < num_features; ++fi) {
- // Both accesses below are safe due to loop bounds for fi.
- const FeatureType *feature_type = feature_vector.type(fi);
- const FeatureValue feature_value = feature_vector.value(fi);
- const int feature_offset =
- concat_offset + feature_type->base() * embedding_dim;
-
- // Code below updates max(0, embedding_dim) elements from concat, starting
- // with index feature_offset. Check below ensures these updates are safe.
- if ((feature_offset < 0) ||
- (feature_offset + embedding_dim > concat_size)) {
- TC_LOG(ERROR) << fi << ": " << feature_offset << " " << embedding_dim
- << " " << concat_size;
- return false;
- }
-
- // Pointer to float / uint8 weights for relevant embedding.
- const void *embedding_data;
-
- // Multiplier for each embedding weight.
- float multiplier;
-
- if (feature_type->is_continuous()) {
- // Continuous features (encoded as FloatFeatureValue).
- FloatFeatureValue float_feature_value(feature_value);
- const int id = float_feature_value.id;
- embedding_matrix->get_embedding(id, &embedding_data, &multiplier);
- multiplier *= float_feature_value.weight;
- } else {
- // Discrete features: every present feature has implicit value 1.0.
- // Hence, after we grab the multiplier below, we don't multiply it by
- // any weight.
- embedding_matrix->get_embedding(feature_value, &embedding_data,
- &multiplier);
- }
-
- // Weighted embeddings will be added starting from this address.
- float *concat_ptr = concat + feature_offset;
-
- if (is_quantized) {
- const uint8 *quant_weights =
- reinterpret_cast<const uint8 *>(embedding_data);
- for (int i = 0; i < embedding_dim; ++i, ++quant_weights, ++concat_ptr) {
- // 128 is bias for UINT8 quantization, only one we currently support.
- *concat_ptr += (static_cast<int>(*quant_weights) - 128) * multiplier;
- }
- } else {
- const float *weights = reinterpret_cast<const float *>(embedding_data);
- for (int i = 0; i < embedding_dim; ++i, ++weights, ++concat_ptr) {
- *concat_ptr += *weights * multiplier;
- }
- }
- }
- return true;
-}
-
-bool EmbeddingNetwork::ComputeLogits(const VectorSpan<float> &input,
- Vector *scores) const {
- return EmbeddingNetwork::ComputeLogitsInternal(input, scores);
-}
-
-bool EmbeddingNetwork::ComputeLogits(const Vector &input,
- Vector *scores) const {
- return EmbeddingNetwork::ComputeLogitsInternal(input, scores);
-}
-
-bool EmbeddingNetwork::ComputeLogitsInternal(const VectorSpan<float> &input,
- Vector *scores) const {
- return FinishComputeFinalScoresInternal<SimpleAdder>(input, scores);
-}
-
-template <typename ScaleAdderClass>
-bool EmbeddingNetwork::FinishComputeFinalScoresInternal(
- const VectorSpan<float> &input, Vector *scores) const {
- // This vector serves as an alternating storage for activations of the
- // different layers. We can't use just one vector here because all of the
- // activations of the previous layer are needed for computation of
- // activations of the next one.
- std::vector<Vector> h_storage(2);
-
- // Compute pre-logits activations.
- VectorSpan<float> h_in(input);
- Vector *h_out;
- for (int i = 0; i < hidden_weights_.size(); ++i) {
- const bool apply_relu = i > 0;
- h_out = &(h_storage[i % 2]);
- h_out->resize(hidden_bias_[i].size());
- if (!SparseReluProductPlusBias<ScaleAdderClass>(
- apply_relu, hidden_weights_[i], hidden_bias_[i], h_in, h_out)) {
- return false;
- }
- h_in = VectorSpan<float>(*h_out);
- }
-
- // Compute logit scores.
- if (!SparseReluProductPlusBias<ScaleAdderClass>(
- true, softmax_weights_, softmax_bias_, h_in, scores)) {
- return false;
- }
-
- return true;
-}
-
-bool EmbeddingNetwork::ComputeFinalScores(
- const std::vector<FeatureVector> &features, Vector *scores) const {
- return ComputeFinalScores(features, {}, scores);
-}
-
-bool EmbeddingNetwork::ComputeFinalScores(
- const std::vector<FeatureVector> &features,
- const std::vector<float> extra_inputs, Vector *scores) const {
- // If we haven't successfully initialized, return without doing anything.
- if (!is_valid()) return false;
-
- Vector concat;
- if (!ConcatEmbeddings(features, &concat)) return false;
-
- if (!extra_inputs.empty()) {
- concat.reserve(concat.size() + extra_inputs.size());
- for (int i = 0; i < extra_inputs.size(); i++) {
- concat.push_back(extra_inputs[i]);
- }
- }
-
- scores->resize(softmax_bias_.size());
- return ComputeLogits(concat, scores);
-}
-
-EmbeddingNetwork::EmbeddingNetwork(const EmbeddingNetworkParams *model) {
- // We'll set valid_ to true only if construction is successful. If we detect
- // an error along the way, we log an informative message and return early, but
- // we do not crash.
- valid_ = false;
-
- // Fill embedding_matrices_, concat_offset_, and concat_layer_size_.
- const int num_embedding_spaces = model->GetNumEmbeddingSpaces();
- int offset_sum = 0;
- for (int i = 0; i < num_embedding_spaces; ++i) {
- concat_offset_.push_back(offset_sum);
- const EmbeddingNetworkParams::Matrix matrix = model->GetEmbeddingMatrix(i);
- if (matrix.quant_type != QuantizationType::UINT8) {
- TC_LOG(ERROR) << "Unsupported quantization for embedding #" << i << ": "
- << static_cast<int>(matrix.quant_type);
- return;
- }
-
- // There is no way to accomodate an empty embedding matrix. E.g., there is
- // no way for get_embedding to return something that can be read safely.
- // Hence, we catch that error here and return early.
- if (matrix.rows == 0) {
- TC_LOG(ERROR) << "Empty embedding matrix #" << i;
- return;
- }
- embedding_matrices_.emplace_back(new EmbeddingMatrix(matrix));
- const int embedding_dim = embedding_matrices_.back()->dim();
- offset_sum += embedding_dim * model->GetNumFeaturesInEmbeddingSpace(i);
- }
- concat_layer_size_ = offset_sum;
-
- // Invariant 2 (trivial by the code above).
- TC_DCHECK_EQ(concat_offset_.size(), embedding_matrices_.size());
-
- const int num_hidden_layers = model->GetNumHiddenLayers();
- if (num_hidden_layers < 1) {
- TC_LOG(ERROR) << "Wrong number of hidden layers: " << num_hidden_layers;
- return;
- }
- hidden_weights_.resize(num_hidden_layers);
- hidden_bias_.resize(num_hidden_layers);
-
- for (int i = 0; i < num_hidden_layers; ++i) {
- const EmbeddingNetworkParams::Matrix matrix =
- model->GetHiddenLayerMatrix(i);
- const EmbeddingNetworkParams::Matrix bias = model->GetHiddenLayerBias(i);
- if (!InitNonQuantizedMatrix(matrix, &hidden_weights_[i]) ||
- !InitNonQuantizedVector(bias, &hidden_bias_[i])) {
- TC_LOG(ERROR) << "Bad hidden layer #" << i;
- return;
- }
- }
-
- if (!model->HasSoftmaxLayer()) {
- TC_LOG(ERROR) << "Missing softmax layer";
- return;
- }
- const EmbeddingNetworkParams::Matrix softmax = model->GetSoftmaxMatrix();
- const EmbeddingNetworkParams::Matrix softmax_bias = model->GetSoftmaxBias();
- if (!InitNonQuantizedMatrix(softmax, &softmax_weights_) ||
- !InitNonQuantizedVector(softmax_bias, &softmax_bias_)) {
- TC_LOG(ERROR) << "Bad softmax layer";
- return;
- }
-
- // Everything looks good.
- valid_ = true;
-}
-
-int EmbeddingNetwork::EmbeddingSize(int es_index) const {
- return embedding_matrices_[es_index]->dim();
-}
-
-} // namespace nlp_core
-} // namespace libtextclassifier
diff --git a/common/embedding-network.h b/common/embedding-network.h
deleted file mode 100644
index a02c6ea..0000000
--- a/common/embedding-network.h
+++ /dev/null
@@ -1,246 +0,0 @@
-/*
- * Copyright (C) 2017 The Android Open Source Project
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#ifndef LIBTEXTCLASSIFIER_COMMON_EMBEDDING_NETWORK_H_
-#define LIBTEXTCLASSIFIER_COMMON_EMBEDDING_NETWORK_H_
-
-#include <memory>
-#include <vector>
-
-#include "common/embedding-network-params.h"
-#include "common/feature-extractor.h"
-#include "common/vector-span.h"
-#include "util/base/integral_types.h"
-#include "util/base/logging.h"
-#include "util/base/macros.h"
-
-namespace libtextclassifier {
-namespace nlp_core {
-
-// Classifier using a hand-coded feed-forward neural network.
-//
-// No gradient computation, just inference.
-//
-// Classification works as follows:
-//
-// Discrete features -> Embeddings -> Concatenation -> Hidden+ -> Softmax
-//
-// In words: given some discrete features, this class extracts the embeddings
-// for these features, concatenates them, passes them through one or two hidden
-// layers (each layer uses Relu) and next through a softmax layer that computes
-// an unnormalized score for each possible class. Note: there is always a
-// softmax layer.
-class EmbeddingNetwork {
- public:
- // Class used to represent an embedding matrix. Each row is the embedding on
- // a vocabulary element. Number of columns = number of embedding dimensions.
- class EmbeddingMatrix {
- public:
- explicit EmbeddingMatrix(const EmbeddingNetworkParams::Matrix source_matrix)
- : rows_(source_matrix.rows),
- cols_(source_matrix.cols),
- quant_type_(source_matrix.quant_type),
- data_(source_matrix.elements),
- row_size_in_bytes_(GetRowSizeInBytes(cols_, quant_type_)),
- quant_scales_(source_matrix.quant_scales) {}
-
- // Returns vocabulary size; one embedding for each vocabulary element.
- int size() const { return rows_; }
-
- // Returns number of weights in embedding of each vocabulary element.
- int dim() const { return cols_; }
-
- // Returns quantization type for this embedding matrix.
- QuantizationType quant_type() const { return quant_type_; }
-
- // Gets embedding for k-th vocabulary element: on return, sets *data to
- // point to the embedding weights and *scale to the quantization scale (1.0
- // if no quantization).
- void get_embedding(int k, const void **data, float *scale) const {
- if ((k < 0) || (k >= size())) {
- TC_LOG(ERROR) << "Index outside [0, " << size() << "): " << k;
-
- // In debug mode, crash. In prod, pretend that k is 0.
- TC_DCHECK(false);
- k = 0;
- }
- *data = reinterpret_cast<const char *>(data_) + k * row_size_in_bytes_;
- if (quant_type_ == QuantizationType::NONE) {
- *scale = 1.0;
- } else {
- *scale = Float16To32(quant_scales_[k]);
- }
- }
-
- private:
- static int GetRowSizeInBytes(int cols, QuantizationType quant_type) {
- switch (quant_type) {
- case QuantizationType::NONE:
- return cols * sizeof(float);
- case QuantizationType::UINT8:
- return cols * sizeof(uint8);
- default:
- TC_LOG(ERROR) << "Unknown quant type: "
- << static_cast<int>(quant_type);
- return 0;
- }
- }
-
- // Vocabulary size.
- const int rows_;
-
- // Number of elements in each embedding.
- const int cols_;
-
- const QuantizationType quant_type_;
-
- // Pointer to the embedding weights, in row-major order. This is a pointer
- // to an array of floats / uint8, depending on the quantization type.
- // Not owned.
- const void *const data_;
-
- // Number of bytes for one row. Used to jump to next row in data_.
- const int row_size_in_bytes_;
-
- // Pointer to quantization scales. nullptr if no quantization. Otherwise,
- // quant_scales_[i] is scale for embedding of i-th vocabulary element.
- const float16 *const quant_scales_;
-
- TC_DISALLOW_COPY_AND_ASSIGN(EmbeddingMatrix);
- };
-
- // An immutable vector that doesn't own the memory that stores the underlying
- // floats. Can be used e.g., as a wrapper around model weights stored in the
- // static memory.
- class VectorWrapper {
- public:
- VectorWrapper() : VectorWrapper(nullptr, 0) {}
-
- // Constructs a vector wrapper around the size consecutive floats that start
- // at address data. Note: the underlying data should be alive for at least
- // the lifetime of this VectorWrapper object. That's trivially true if data
- // points to statically allocated data :)
- VectorWrapper(const float *data, int size) : data_(data), size_(size) {}
-
- int size() const { return size_; }
-
- const float *data() const { return data_; }
-
- private:
- const float *data_; // Not owned.
- int size_;
-
- // Doesn't own anything, so it can be copied and assigned at will :)
- };
-
- typedef std::vector<VectorWrapper> Matrix;
- typedef std::vector<float> Vector;
-
- // Constructs an embedding network using the parameters from model.
- //
- // Note: model should stay alive for at least the lifetime of this
- // EmbeddingNetwork object.
- explicit EmbeddingNetwork(const EmbeddingNetworkParams *model);
-
- virtual ~EmbeddingNetwork() {}
-
- // Returns true if this EmbeddingNetwork object has been correctly constructed
- // and is ready to use. Idea: in case of errors, mark this EmbeddingNetwork
- // object as invalid, but do not crash.
- bool is_valid() const { return valid_; }
-
- // Runs forward computation to fill scores with unnormalized output unit
- // scores. This is useful for making predictions.
- //
- // Returns true on success, false on error (e.g., if !is_valid()).
- bool ComputeFinalScores(const std::vector<FeatureVector> &features,
- Vector *scores) const;
-
- // Same as above, but allows specification of extra neural network inputs that
- // will be appended to the embedding vector build from features.
- bool ComputeFinalScores(const std::vector<FeatureVector> &features,
- const std::vector<float> extra_inputs,
- Vector *scores) const;
-
- // Constructs the concatenated input embedding vector in place in output
- // vector concat. Returns true on success, false on error.
- bool ConcatEmbeddings(const std::vector<FeatureVector> &features,
- Vector *concat) const;
-
- // Sums embeddings for all features from |feature_vector| and adds result
- // to values from the array pointed-to by |output|. Embeddings for continuous
- // features are weighted by the feature weight.
- //
- // NOTE: output should point to an array of EmbeddingSize(es_index) floats.
- bool GetEmbedding(const FeatureVector &feature_vector, int es_index,
- float *embedding) const;
-
- // Runs the feed-forward neural network for |input| and computes logits for
- // softmax layer.
- bool ComputeLogits(const Vector &input, Vector *scores) const;
-
- // Same as above but uses a view of the feature vector.
- bool ComputeLogits(const VectorSpan<float> &input, Vector *scores) const;
-
- // Returns the size (the number of columns) of the embedding space es_index.
- int EmbeddingSize(int es_index) const;
-
- protected:
- // Builds an embedding for given feature vector, and places it from
- // concat_offset to the concat vector.
- bool GetEmbeddingInternal(const FeatureVector &feature_vector,
- EmbeddingMatrix *embedding_matrix,
- int concat_offset, float *concat,
- int embedding_size) const;
-
- // Templated function that computes the logit scores given the concatenated
- // input embeddings.
- bool ComputeLogitsInternal(const VectorSpan<float> &concat,
- Vector *scores) const;
-
- // Computes the softmax scores (prior to normalization) from the concatenated
- // representation. Returns true on success, false on error.
- template <typename ScaleAdderClass>
- bool FinishComputeFinalScoresInternal(const VectorSpan<float> &concat,
- Vector *scores) const;
-
- // Set to true on successful construction, false otherwise.
- bool valid_ = false;
-
- // Network parameters.
-
- // One weight matrix for each embedding space.
- std::vector<std::unique_ptr<EmbeddingMatrix>> embedding_matrices_;
-
- // concat_offset_[i] is the input layer offset for i-th embedding space.
- std::vector<int> concat_offset_;
-
- // Size of the input ("concatenation") layer.
- int concat_layer_size_;
-
- // One weight matrix and one vector of bias weights for each hiden layer.
- std::vector<Matrix> hidden_weights_;
- std::vector<VectorWrapper> hidden_bias_;
-
- // Weight matrix and bias vector for the softmax layer.
- Matrix softmax_weights_;
- VectorWrapper softmax_bias_;
-};
-
-} // namespace nlp_core
-} // namespace libtextclassifier
-
-#endif // LIBTEXTCLASSIFIER_COMMON_EMBEDDING_NETWORK_H_
diff --git a/common/embedding-network.proto b/common/embedding-network.proto
deleted file mode 100644
index ce30b11..0000000
--- a/common/embedding-network.proto
+++ /dev/null
@@ -1,90 +0,0 @@
-// Copyright (C) 2017 The Android Open Source Project
-//
-// Licensed under the Apache License, Version 2.0 (the "License");
-// you may not use this file except in compliance with the License.
-// You may obtain a copy of the License at
-//
-// http://www.apache.org/licenses/LICENSE-2.0
-//
-// Unless required by applicable law or agreed to in writing, software
-// distributed under the License is distributed on an "AS IS" BASIS,
-// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-// See the License for the specific language governing permissions and
-// limitations under the License.
-
-// Protos for performing inference with an EmbeddingNetwork.
-
-syntax = "proto2";
-option optimize_for = LITE_RUNTIME;
-
-package libtextclassifier.nlp_core;
-
-// Wrapper for storing a matrix of parameters. These are stored in row-major
-// order.
-message MatrixParams {
- optional int32 rows = 1; // # of rows in the matrix
- optional int32 cols = 2; // # of columns in the matrix
-
- // Non-quantized matrix entries.
- repeated float value = 3 [packed = true];
-
- // Whether the matrix is quantized.
- optional bool is_quantized = 4 [default = false];
-
- // Bytes for all quantized values. Each value (see "repeated float value"
- // field) is quantized to an uint8 (1 byte) value, and all these bytes are
- // concatenated into the string from this field.
- optional bytes bytes_for_quantized_values = 7;
-
- // Bytes for all scale factors for dequantizing the values. The quantization
- // process generates a float16 scale factor for each column. The 2 bytes for
- // each such float16 are put in little-endian order (least significant byte
- // first) and next all these pairs of bytes are concatenated into the string
- // from this field.
- optional bytes bytes_for_col_scales = 8;
-
- reserved 5, 6;
-}
-
-// Stores all parameters for a given EmbeddingNetwork. This can either be a
-// EmbeddingNetwork or a PrecomputedEmbeddingNetwork: for precomputed networks,
-// the embedding weights are actually the activations of the first hidden layer
-// *before* the bias is added and the non-linear transform is applied.
-//
-// Thus, for PrecomputedEmbeddingNetwork storage, hidden layers are stored
-// starting from the second hidden layer, while biases are stored for every
-// hidden layer.
-message EmbeddingNetworkProto {
- // Embeddings and hidden layers. Note that if is_precomputed == true, then the
- // embeddings should store the activations of the first hidden layer, so we
- // must have hidden_bias_size() == hidden_size() + 1 (we store weights for
- // first hidden layer bias, but no the layer itself.)
- repeated MatrixParams embeddings = 1;
- repeated MatrixParams hidden = 2;
- repeated MatrixParams hidden_bias = 3;
-
- // Final layer of the network.
- optional MatrixParams softmax = 4;
- optional MatrixParams softmax_bias = 5;
-
- // Element i of the repeated field below indicates number of features that use
- // the i-th embedding space.
- repeated int32 embedding_num_features = 7;
-
- // Whether or not this is intended to store a precomputed network.
- optional bool is_precomputed = 11 [default = false];
-
- // True if this EmbeddingNetworkProto can be used for inference with no
- // additional matrix transposition.
- //
- // Given an EmbeddingNetworkProto produced by a Neurosis training pipeline, we
- // have to transpose a few matrices (e.g., the embedding matrices) before we
- // can perform inference. When we do so, we negate this flag. Note: we don't
- // simply set this to true: transposing twice takes us to the original state.
- optional bool is_transposed = 12 [default = false];
-
- // Allow extensions.
- extensions 100 to max;
-
- reserved 6, 8, 9, 10;
-}
diff --git a/common/embedding-network_test.cc b/common/embedding-network_test.cc
deleted file mode 100644
index 026ec17..0000000
--- a/common/embedding-network_test.cc
+++ /dev/null
@@ -1,87 +0,0 @@
-/*
- * Copyright (C) 2017 The Android Open Source Project
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#include "common/embedding-network.h"
-#include "common/embedding-network-params-from-proto.h"
-#include "common/embedding-network.pb.h"
-#include "common/simple-adder.h"
-
-#include "gmock/gmock.h"
-#include "gtest/gtest.h"
-
-namespace libtextclassifier {
-namespace nlp_core {
-namespace {
-
-using testing::ElementsAreArray;
-
-class TestingEmbeddingNetwork : public EmbeddingNetwork {
- public:
- using EmbeddingNetwork::EmbeddingNetwork;
- using EmbeddingNetwork::FinishComputeFinalScoresInternal;
-};
-
-void DiagonalAndBias3x3(int diagonal_value, int bias_value,
- MatrixParams* weights, MatrixParams* bias) {
- weights->set_rows(3);
- weights->set_cols(3);
- weights->add_value(diagonal_value);
- weights->add_value(0);
- weights->add_value(0);
- weights->add_value(0);
- weights->add_value(diagonal_value);
- weights->add_value(0);
- weights->add_value(0);
- weights->add_value(0);
- weights->add_value(diagonal_value);
-
- bias->set_rows(3);
- bias->set_cols(1);
- bias->add_value(bias_value);
- bias->add_value(bias_value);
- bias->add_value(bias_value);
-}
-
-TEST(EmbeddingNetworkTest, IdentityThroughMultipleLayers) {
- std::unique_ptr<EmbeddingNetworkProto> proto;
- proto.reset(new EmbeddingNetworkProto);
-
- // These layers should be an identity with bias.
- DiagonalAndBias3x3(/*diagonal_value=*/1, /*bias_value=*/1,
- proto->add_hidden(), proto->add_hidden_bias());
- DiagonalAndBias3x3(/*diagonal_value=*/1, /*bias_value=*/2,
- proto->add_hidden(), proto->add_hidden_bias());
- DiagonalAndBias3x3(/*diagonal_value=*/1, /*bias_value=*/3,
- proto->add_hidden(), proto->add_hidden_bias());
- DiagonalAndBias3x3(/*diagonal_value=*/1, /*bias_value=*/4,
- proto->add_hidden(), proto->add_hidden_bias());
- DiagonalAndBias3x3(/*diagonal_value=*/1, /*bias_value=*/5,
- proto->mutable_softmax(), proto->mutable_softmax_bias());
-
- EmbeddingNetworkParamsFromProto params(std::move(proto));
- TestingEmbeddingNetwork network(¶ms);
-
- std::vector<float> input({-2, -1, 0});
- std::vector<float> output;
- network.FinishComputeFinalScoresInternal<SimpleAdder>(
- VectorSpan<float>(input), &output);
-
- EXPECT_THAT(output, ElementsAreArray({14, 14, 15}));
-}
-
-} // namespace
-} // namespace nlp_core
-} // namespace libtextclassifier
diff --git a/common/feature-descriptors.h b/common/feature-descriptors.h
deleted file mode 100644
index 9aa6527..0000000
--- a/common/feature-descriptors.h
+++ /dev/null
@@ -1,154 +0,0 @@
-/*
- * Copyright (C) 2017 The Android Open Source Project
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#ifndef LIBTEXTCLASSIFIER_COMMON_FEATURE_DESCRIPTORS_H_
-#define LIBTEXTCLASSIFIER_COMMON_FEATURE_DESCRIPTORS_H_
-
-#include <memory>
-#include <string>
-#include <vector>
-
-#include "util/base/integral_types.h"
-#include "util/base/logging.h"
-#include "util/base/macros.h"
-
-namespace libtextclassifier {
-namespace nlp_core {
-
-// Named feature parameter.
-class Parameter {
- public:
- Parameter() {}
-
- void set_name(const std::string &name) { name_ = name; }
- const std::string &name() const { return name_; }
-
- void set_value(const std::string &value) { value_ = value; }
- const std::string &value() const { return value_; }
-
- private:
- std::string name_;
- std::string value_;
-};
-
-// Descriptor for a feature function. Used to store the results of parsing one
-// feature function.
-class FeatureFunctionDescriptor {
- public:
- FeatureFunctionDescriptor() {}
-
- // Accessors for the feature function type. The function type is the string
- // that the feature extractor code is registered under.
- void set_type(const std::string &type) { type_ = type; }
- bool has_type() const { return !type_.empty(); }
- const std::string &type() const { return type_; }
-
- // Accessors for the feature function name. The function name (if available)
- // is used for some log messages. Otherwise, a more precise, but also more
- // verbose name based on the feature specification is used.
- void set_name(const std::string &name) { name_ = name; }
- bool has_name() const { return !name_.empty(); }
- const std::string &name() { return name_; }
-
- // Accessors for the default (name-less) parameter.
- void set_argument(int32 argument) { argument_ = argument; }
- bool has_argument() const {
- // If argument has not been specified, clients should treat it as 0. This
- // makes the test below correct, without having a separate has_argument_
- // bool field.
- return argument_ != 0;
- }
- int32 argument() const { return argument_; }
-
- // Accessors for the named parameters.
- Parameter *add_parameter() {
- parameters_.emplace_back();
- return &(parameters_.back());
- }
- int parameter_size() const { return parameters_.size(); }
- const Parameter ¶meter(int i) const {
- TC_DCHECK((i >= 0) && (i < parameter_size()));
- return parameters_[i];
- }
-
- // Accessors for the sub (i.e., nested) features. Nested features: as in
- // offset(1).label.
- FeatureFunctionDescriptor *add_feature() {
- sub_features_.emplace_back(new FeatureFunctionDescriptor());
- return sub_features_.back().get();
- }
- int feature_size() const { return sub_features_.size(); }
- const FeatureFunctionDescriptor &feature(int i) const {
- TC_DCHECK((i >= 0) && (i < feature_size()));
- return *(sub_features_[i].get());
- }
- FeatureFunctionDescriptor *mutable_feature(int i) {
- TC_DCHECK((i >= 0) && (i < feature_size()));
- return sub_features_[i].get();
- }
-
- private:
- // See comments for set_type().
- std::string type_;
-
- // See comments for set_name().
- std::string name_;
-
- // See comments for set_argument().
- int32 argument_ = 0;
-
- // See comemnts for add_parameter().
- std::vector<Parameter> parameters_;
-
- // See comments for add_feature().
- std::vector<std::unique_ptr<FeatureFunctionDescriptor>> sub_features_;
-
- TC_DISALLOW_COPY_AND_ASSIGN(FeatureFunctionDescriptor);
-};
-
-// List of FeatureFunctionDescriptors. Used to store the result of parsing the
-// spec for several feature functions.
-class FeatureExtractorDescriptor {
- public:
- FeatureExtractorDescriptor() {}
-
- int feature_size() const { return features_.size(); }
-
- FeatureFunctionDescriptor *add_feature() {
- features_.emplace_back(new FeatureFunctionDescriptor());
- return features_.back().get();
- }
-
- const FeatureFunctionDescriptor &feature(int i) const {
- TC_DCHECK((i >= 0) && (i < feature_size()));
- return *(features_[i].get());
- }
-
- FeatureFunctionDescriptor *mutable_feature(int i) {
- TC_DCHECK((i >= 0) && (i < feature_size()));
- return features_[i].get();
- }
-
- private:
- std::vector<std::unique_ptr<FeatureFunctionDescriptor>> features_;
-
- TC_DISALLOW_COPY_AND_ASSIGN(FeatureExtractorDescriptor);
-};
-
-} // namespace nlp_core
-} // namespace libtextclassifier
-
-#endif // LIBTEXTCLASSIFIER_COMMON_FEATURE_DESCRIPTORS_H_
diff --git a/common/feature-extractor.cc b/common/feature-extractor.cc
deleted file mode 100644
index 12de46d..0000000
--- a/common/feature-extractor.cc
+++ /dev/null
@@ -1,152 +0,0 @@
-/*
- * Copyright (C) 2017 The Android Open Source Project
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#include "common/feature-extractor.h"
-
-#include "common/feature-types.h"
-#include "common/fml-parser.h"
-#include "util/base/integral_types.h"
-#include "util/base/logging.h"
-#include "util/gtl/stl_util.h"
-#include "util/strings/numbers.h"
-
-namespace libtextclassifier {
-namespace nlp_core {
-
-constexpr FeatureValue GenericFeatureFunction::kNone;
-
-GenericFeatureExtractor::GenericFeatureExtractor() {}
-
-GenericFeatureExtractor::~GenericFeatureExtractor() {}
-
-bool GenericFeatureExtractor::Parse(const std::string &source) {
- // Parse feature specification into descriptor.
- FMLParser parser;
- if (!parser.Parse(source, mutable_descriptor())) return false;
-
- // Initialize feature extractor from descriptor.
- if (!InitializeFeatureFunctions()) return false;
- return true;
-}
-
-bool GenericFeatureExtractor::InitializeFeatureTypes() {
- // Register all feature types.
- GetFeatureTypes(&feature_types_);
- for (size_t i = 0; i < feature_types_.size(); ++i) {
- FeatureType *ft = feature_types_[i];
- ft->set_base(i);
-
- // Check for feature space overflow.
- double domain_size = ft->GetDomainSize();
- if (domain_size < 0) {
- TC_LOG(ERROR) << "Illegal domain size for feature " << ft->name() << ": "
- << domain_size;
- return false;
- }
- }
- return true;
-}
-
-FeatureValue GenericFeatureExtractor::GetDomainSize() const {
- // Domain size of the set of features is equal to:
- // [largest domain size of any feature types] * [number of feature types]
- FeatureValue max_feature_type_dsize = 0;
- for (size_t i = 0; i < feature_types_.size(); ++i) {
- FeatureType *ft = feature_types_[i];
- const FeatureValue feature_type_dsize = ft->GetDomainSize();
- if (feature_type_dsize > max_feature_type_dsize) {
- max_feature_type_dsize = feature_type_dsize;
- }
- }
-
- return max_feature_type_dsize * feature_types_.size();
-}
-
-std::string GenericFeatureFunction::GetParameter(
- const std::string &name) const {
- // Find named parameter in feature descriptor.
- for (int i = 0; i < descriptor_->parameter_size(); ++i) {
- if (name == descriptor_->parameter(i).name()) {
- return descriptor_->parameter(i).value();
- }
- }
- return "";
-}
-
-GenericFeatureFunction::GenericFeatureFunction() {}
-
-GenericFeatureFunction::~GenericFeatureFunction() { delete feature_type_; }
-
-int GenericFeatureFunction::GetIntParameter(const std::string &name,
- int default_value) const {
- int32 parsed_value = default_value;
- std::string value = GetParameter(name);
- if (!value.empty()) {
- if (!ParseInt32(value.c_str(), &parsed_value)) {
- // A parameter value has been specified, but it can't be parsed as an int.
- // We don't crash: instead, we long an error and return the default value.
- TC_LOG(ERROR) << "Value of param " << name << " is not an int: " << value;
- }
- }
- return parsed_value;
-}
-
-bool GenericFeatureFunction::GetBoolParameter(const std::string &name,
- bool default_value) const {
- std::string value = GetParameter(name);
- if (value.empty()) return default_value;
- if (value == "true") return true;
- if (value == "false") return false;
- TC_LOG(ERROR) << "Illegal value '" << value << "' for bool parameter '"
- << name << "'"
- << " will assume default " << default_value;
- return default_value;
-}
-
-void GenericFeatureFunction::GetFeatureTypes(
- std::vector<FeatureType *> *types) const {
- if (feature_type_ != nullptr) types->push_back(feature_type_);
-}
-
-FeatureType *GenericFeatureFunction::GetFeatureType() const {
- // If a single feature type has been registered return it.
- if (feature_type_ != nullptr) return feature_type_;
-
- // Get feature types for function.
- std::vector<FeatureType *> types;
- GetFeatureTypes(&types);
-
- // If there is exactly one feature type return this, else return null.
- if (types.size() == 1) return types[0];
- return nullptr;
-}
-
-std::string GenericFeatureFunction::name() const {
- std::string output;
- if (descriptor_->name().empty()) {
- if (!prefix_.empty()) {
- output.append(prefix_);
- output.append(".");
- }
- ToFML(*descriptor_, &output);
- } else {
- output = descriptor_->name();
- }
- return output;
-}
-
-} // namespace nlp_core
-} // namespace libtextclassifier
diff --git a/common/feature-extractor.h b/common/feature-extractor.h
deleted file mode 100644
index bdba609..0000000
--- a/common/feature-extractor.h
+++ /dev/null
@@ -1,665 +0,0 @@
-/*
- * Copyright (C) 2017 The Android Open Source Project
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-// Generic feature extractor for extracting features from objects. The feature
-// extractor can be used for extracting features from any object. The feature
-// extractor and feature function classes are template classes that have to
-// be instantiated for extracting feature from a specific object type.
-//
-// A feature extractor consists of a hierarchy of feature functions. Each
-// feature function extracts one or more feature type and value pairs from the
-// object.
-//
-// The feature extractor has a modular design where new feature functions can be
-// registered as components. The feature extractor is initialized from a
-// descriptor represented by a protocol buffer. The feature extractor can also
-// be initialized from a text-based source specification of the feature
-// extractor. Feature specification parsers can be added as components. By
-// default the feature extractor can be read from an ASCII protocol buffer or in
-// a simple feature modeling language (fml).
-
-// A feature function is invoked with a focus. Nested feature function can be
-// invoked with another focus determined by the parent feature function.
-
-#ifndef LIBTEXTCLASSIFIER_COMMON_FEATURE_EXTRACTOR_H_
-#define LIBTEXTCLASSIFIER_COMMON_FEATURE_EXTRACTOR_H_
-
-#include <stddef.h>
-
-#include <string>
-#include <vector>
-
-#include "common/feature-descriptors.h"
-#include "common/feature-types.h"
-#include "common/fml-parser.h"
-#include "common/registry.h"
-#include "common/task-context.h"
-#include "common/workspace.h"
-#include "util/base/integral_types.h"
-#include "util/base/logging.h"
-#include "util/base/macros.h"
-#include "util/gtl/stl_util.h"
-
-namespace libtextclassifier {
-namespace nlp_core {
-
-typedef int64 Predicate;
-typedef Predicate FeatureValue;
-
-// A union used to represent discrete and continuous feature values.
-union FloatFeatureValue {
- public:
- explicit FloatFeatureValue(FeatureValue v) : discrete_value(v) {}
- FloatFeatureValue(uint32 i, float w) : id(i), weight(w) {}
- FeatureValue discrete_value;
- struct {
- uint32 id;
- float weight;
- };
-};
-
-// A feature vector contains feature type and value pairs.
-class FeatureVector {
- public:
- FeatureVector() {}
-
- // Adds feature type and value pair to feature vector.
- void add(FeatureType *type, FeatureValue value) {
- features_.emplace_back(type, value);
- }
-
- // Removes all elements from the feature vector.
- void clear() { features_.clear(); }
-
- // Returns the number of elements in the feature vector.
- int size() const { return features_.size(); }
-
- // Reserves space in the underlying feature vector.
- void reserve(int n) { features_.reserve(n); }
-
- // Returns feature type for an element in the feature vector.
- FeatureType *type(int index) const { return features_[index].type; }
-
- // Returns feature value for an element in the feature vector.
- FeatureValue value(int index) const { return features_[index].value; }
-
- private:
- // Structure for holding feature type and value pairs.
- struct Element {
- Element() : type(nullptr), value(-1) {}
- Element(FeatureType *t, FeatureValue v) : type(t), value(v) {}
-
- FeatureType *type;
- FeatureValue value;
- };
-
- // Array for storing feature vector elements.
- std::vector<Element> features_;
-
- TC_DISALLOW_COPY_AND_ASSIGN(FeatureVector);
-};
-
-// The generic feature extractor is the type-independent part of a feature
-// extractor. This holds the descriptor for the feature extractor and the
-// collection of feature types used in the feature extractor. The feature
-// types are not available until FeatureExtractor<>::Init() has been called.
-class GenericFeatureExtractor {
- public:
- GenericFeatureExtractor();
- virtual ~GenericFeatureExtractor();
-
- // Initializes the feature extractor from an FML string specification. For
- // the FML specification grammar, see fml-parser.h.
- //
- // Returns true on success, false on syntax error.
- bool Parse(const std::string &source);
-
- // Returns the feature extractor descriptor.
- const FeatureExtractorDescriptor &descriptor() const { return descriptor_; }
- FeatureExtractorDescriptor *mutable_descriptor() { return &descriptor_; }
-
- // Returns the number of feature types in the feature extractor. Invalid
- // before Init() has been called.
- int feature_types() const { return feature_types_.size(); }
-
- // Returns a feature type used in the extractor. Invalid before Init() has
- // been called.
- const FeatureType *feature_type(int index) const {
- return feature_types_[index];
- }
-
- // Returns the feature domain size of this feature extractor.
- // NOTE: The way that domain size is calculated is, for some, unintuitive. It
- // is the largest domain size of any feature type.
- FeatureValue GetDomainSize() const;
-
- protected:
- // Initializes the feature types used by the extractor. Called from
- // FeatureExtractor<>::Init().
- //
- // Returns true on success, false on error.
- bool InitializeFeatureTypes();
-
- private:
- // Initializes the top-level feature functions.
- virtual bool InitializeFeatureFunctions() = 0;
-
- // Returns all feature types used by the extractor. The feature types are
- // added to the result array.
- virtual void GetFeatureTypes(std::vector<FeatureType *> *types) const = 0;
-
- // Descriptor for the feature extractor. This is a protocol buffer that
- // contains all the information about the feature extractor. The feature
- // functions are initialized from the information in the descriptor.
- FeatureExtractorDescriptor descriptor_;
-
- // All feature types used by the feature extractor. The collection of all the
- // feature types describes the feature space of the feature set produced by
- // the feature extractor. Not owned.
- std::vector<FeatureType *> feature_types_;
-
- TC_DISALLOW_COPY_AND_ASSIGN(GenericFeatureExtractor);
-};
-
-// The generic feature function is the type-independent part of a feature
-// function. Each feature function is associated with the descriptor that it is
-// instantiated from. The feature types associated with this feature function
-// will be established by the time FeatureExtractor<>::Init() completes.
-class GenericFeatureFunction {
- public:
- // A feature value that represents the absence of a value.
- static constexpr FeatureValue kNone = -1;
-
- GenericFeatureFunction();
- virtual ~GenericFeatureFunction();
-
- // Sets up the feature function. NB: FeatureTypes of nested functions are not
- // guaranteed to be available until Init().
- //
- // Returns true on success, false on error.
- virtual bool Setup(TaskContext *context) { return true; }
-
- // Initializes the feature function. NB: The FeatureType of this function must
- // be established when this method completes.
- //
- // Returns true on success, false on error.
- virtual bool Init(TaskContext *context) { return true; }
-
- // Requests workspaces from a registry to obtain indices into a WorkspaceSet
- // for any Workspace objects used by this feature function. NB: This will be
- // called after Init(), so it can depend on resources and arguments.
- virtual void RequestWorkspaces(WorkspaceRegistry *registry) {}
-
- // Appends the feature types produced by the feature function to types. The
- // default implementation appends feature_type(), if non-null. Invalid
- // before Init() has been called.
- virtual void GetFeatureTypes(std::vector<FeatureType *> *types) const;
-
- // Returns the feature type for feature produced by this feature function. If
- // the feature function produces features of different types this returns
- // null. Invalid before Init() has been called.
- virtual FeatureType *GetFeatureType() const;
-
- // Returns the name of the registry used for creating the feature function.
- // This can be used for checking if two feature functions are of the same
- // kind.
- virtual const char *RegistryName() const = 0;
-
- // Returns the value of a named parameter from the feature function
- // descriptor. Returns empty string ("") if parameter is not found.
- std::string GetParameter(const std::string &name) const;
-
- // Returns the int value of a named parameter from the feature function
- // descriptor. Returns default_value if the parameter is not found or if its
- // value can't be parsed as an int.
- int GetIntParameter(const std::string &name, int default_value) const;
-
- // Returns the bool value of a named parameter from the feature function
- // descriptor. Returns default_value if the parameter is not found or if its
- // value is not "true" or "false".
- bool GetBoolParameter(const std::string &name, bool default_value) const;
-
- // Returns the FML function description for the feature function, i.e. the
- // name and parameters without the nested features.
- std::string FunctionName() const {
- std::string output;
- ToFMLFunction(*descriptor_, &output);
- return output;
- }
-
- // Returns the prefix for nested feature functions. This is the prefix of this
- // feature function concatenated with the feature function name.
- std::string SubPrefix() const {
- return prefix_.empty() ? FunctionName() : prefix_ + "." + FunctionName();
- }
-
- // Returns/sets the feature extractor this function belongs to.
- GenericFeatureExtractor *extractor() const { return extractor_; }
- void set_extractor(GenericFeatureExtractor *extractor) {
- extractor_ = extractor;
- }
-
- // Returns/sets the feature function descriptor.
- FeatureFunctionDescriptor *descriptor() const { return descriptor_; }
- void set_descriptor(FeatureFunctionDescriptor *descriptor) {
- descriptor_ = descriptor;
- }
-
- // Returns a descriptive name for the feature function. The name is taken from
- // the descriptor for the feature function. If the name is empty or the
- // feature function is a variable the name is the FML representation of the
- // feature, including the prefix.
- std::string name() const;
-
- // Returns the argument from the feature function descriptor. It defaults to
- // 0 if the argument has not been specified.
- int argument() const {
- return descriptor_->has_argument() ? descriptor_->argument() : 0;
- }
-
- // Returns/sets/clears function name prefix.
- const std::string &prefix() const { return prefix_; }
- void set_prefix(const std::string &prefix) { prefix_ = prefix; }
-
- protected:
- // Returns the feature type for single-type feature functions.
- FeatureType *feature_type() const { return feature_type_; }
-
- // Sets the feature type for single-type feature functions. This takes
- // ownership of feature_type. Can only be called once with a non-null
- // pointer.
- void set_feature_type(FeatureType *feature_type) {
- TC_DCHECK_NE(feature_type, nullptr);
- feature_type_ = feature_type;
- }
-
- private:
- // Feature extractor this feature function belongs to. Not owned.
- GenericFeatureExtractor *extractor_ = nullptr;
-
- // Descriptor for feature function. Not owned.
- FeatureFunctionDescriptor *descriptor_ = nullptr;
-
- // Feature type for features produced by this feature function. If the
- // feature function produces features of multiple feature types this is null
- // and the feature function must return it's feature types in
- // GetFeatureTypes(). Owned.
- FeatureType *feature_type_ = nullptr;
-
- // Prefix used for sub-feature types of this function.
- std::string prefix_;
-};
-
-// Feature function that can extract features from an object. Templated on
-// two type arguments:
-//
-// OBJ: The "object" from which features are extracted; e.g., a sentence. This
-// should be a plain type, rather than a reference or pointer.
-//
-// ARGS: A set of 0 or more types that are used to "index" into some part of the
-// object that should be extracted, e.g. an int token index for a sentence
-// object. This should not be a reference type.
-template <class OBJ, class... ARGS>
-class FeatureFunction
- : public GenericFeatureFunction,
- public RegisterableClass<FeatureFunction<OBJ, ARGS...> > {
- public:
- using Self = FeatureFunction<OBJ, ARGS...>;
-
- // Preprocesses the object. This will be called prior to calling Evaluate()
- // or Compute() on that object.
- virtual void Preprocess(WorkspaceSet *workspaces, OBJ *object) const {}
-
- // Appends features computed from the object and focus to the result. The
- // default implementation delegates to Compute(), adding a single value if
- // available. Multi-valued feature functions must override this method.
- virtual void Evaluate(const WorkspaceSet &workspaces, const OBJ &object,
- ARGS... args, FeatureVector *result) const {
- FeatureValue value = Compute(workspaces, object, args..., result);
- if (value != kNone) result->add(feature_type(), value);
- }
-
- // Returns a feature value computed from the object and focus, or kNone if no
- // value is computed. Single-valued feature functions only need to override
- // this method.
- virtual FeatureValue Compute(const WorkspaceSet &workspaces,
- const OBJ &object, ARGS... args,
- const FeatureVector *fv) const {
- return kNone;
- }
-
- // Instantiates a new feature function in a feature extractor from a feature
- // descriptor.
- static Self *Instantiate(GenericFeatureExtractor *extractor,
- FeatureFunctionDescriptor *fd,
- const std::string &prefix) {
- Self *f = Self::Create(fd->type());
- if (f != nullptr) {
- f->set_extractor(extractor);
- f->set_descriptor(fd);
- f->set_prefix(prefix);
- }
- return f;
- }
-
- // Returns the name of the registry for the feature function.
- const char *RegistryName() const override { return Self::registry()->name(); }
-
- private:
- // Special feature function class for resolving variable references. The type
- // of the feature function is used for resolving the variable reference. When
- // evaluated it will either get the feature value(s) from the variable portion
- // of the feature vector, if present, or otherwise it will call the referenced
- // feature extractor function directly to extract the feature(s).
- class Reference;
-};
-
-// Base class for features with nested feature functions. The nested functions
-// are of type NES, which may be different from the type of the parent function.
-// NB: NestedFeatureFunction will ensure that all initialization of nested
-// functions takes place during Setup() and Init() -- after the nested features
-// are initialized, the parent feature is initialized via SetupNested() and
-// InitNested(). Alternatively, a derived classes that overrides Setup() and
-// Init() directly should call Parent::Setup(), Parent::Init(), etc. first.
-//
-// Note: NestedFeatureFunction cannot know how to call Preprocess, Evaluate, or
-// Compute, since the nested functions may be of a different type.
-template <class NES, class OBJ, class... ARGS>
-class NestedFeatureFunction : public FeatureFunction<OBJ, ARGS...> {
- public:
- using Parent = NestedFeatureFunction<NES, OBJ, ARGS...>;
-
- // Clean up nested functions.
- ~NestedFeatureFunction() override {
- // Fully qualified class name, to avoid an ambiguity error when building for
- // Android.
- ::libtextclassifier::STLDeleteElements(&nested_);
- }
-
- // By default, just appends the nested feature types.
- void GetFeatureTypes(std::vector<FeatureType *> *types) const override {
- // It's odd if a NestedFeatureFunction does not have anything nested inside
- // it, so we crash in debug mode. Still, nothing should crash in prod mode.
- TC_DCHECK(!this->nested().empty())
- << "Nested features require nested features to be defined.";
- for (auto *function : nested_) function->GetFeatureTypes(types);
- }
-
- // Sets up the nested features.
- bool Setup(TaskContext *context) override {
- bool success = CreateNested(this->extractor(), this->descriptor(), &nested_,
- this->SubPrefix());
- if (!success) {
- return false;
- }
- for (auto *function : nested_) {
- if (!function->Setup(context)) return false;
- }
- if (!SetupNested(context)) {
- return false;
- }
- return true;
- }
-
- // Sets up this NestedFeatureFunction specifically.
- virtual bool SetupNested(TaskContext *context) { return true; }
-
- // Initializes the nested features.
- bool Init(TaskContext *context) override {
- for (auto *function : nested_) {
- if (!function->Init(context)) return false;
- }
- if (!InitNested(context)) return false;
- return true;
- }
-
- // Initializes this NestedFeatureFunction specifically.
- virtual bool InitNested(TaskContext *context) { return true; }
-
- // Gets all the workspaces needed for the nested functions.
- void RequestWorkspaces(WorkspaceRegistry *registry) override {
- for (auto *function : nested_) function->RequestWorkspaces(registry);
- }
-
- // Returns the list of nested feature functions.
- const std::vector<NES *> &nested() const { return nested_; }
-
- // Instantiates nested feature functions for a feature function. Creates and
- // initializes one feature function for each sub-descriptor in the feature
- // descriptor.
- static bool CreateNested(GenericFeatureExtractor *extractor,
- FeatureFunctionDescriptor *fd,
- std::vector<NES *> *functions,
- const std::string &prefix) {
- for (int i = 0; i < fd->feature_size(); ++i) {
- FeatureFunctionDescriptor *sub = fd->mutable_feature(i);
- NES *f = NES::Instantiate(extractor, sub, prefix);
- if (f == nullptr) {
- return false;
- }
- functions->push_back(f);
- }
- return true;
- }
-
- protected:
- // The nested feature functions, if any, in order of declaration in the
- // feature descriptor. Owned.
- std::vector<NES *> nested_;
-};
-
-// Base class for a nested feature function that takes nested features with the
-// same signature as these features, i.e. a meta feature. For this class, we can
-// provide preprocessing of the nested features.
-template <class OBJ, class... ARGS>
-class MetaFeatureFunction
- : public NestedFeatureFunction<FeatureFunction<OBJ, ARGS...>, OBJ,
- ARGS...> {
- public:
- // Preprocesses using the nested features.
- void Preprocess(WorkspaceSet *workspaces, OBJ *object) const override {
- for (auto *function : this->nested_) {
- function->Preprocess(workspaces, object);
- }
- }
-};
-
-// Template for a special type of locator: The locator of type
-// FeatureFunction<OBJ, ARGS...> calls nested functions of type
-// FeatureFunction<OBJ, IDX, ARGS...>, where the derived class DER is
-// responsible for translating by providing the following:
-//
-// // Gets the new additional focus.
-// IDX GetFocus(const WorkspaceSet &workspaces, const OBJ &object);
-//
-// This is useful to e.g. add a token focus to a parser state based on some
-// desired property of that state.
-template <class DER, class OBJ, class IDX, class... ARGS>
-class FeatureAddFocusLocator
- : public NestedFeatureFunction<FeatureFunction<OBJ, IDX, ARGS...>, OBJ,
- ARGS...> {
- public:
- void Preprocess(WorkspaceSet *workspaces, OBJ *object) const override {
- for (auto *function : this->nested_) {
- function->Preprocess(workspaces, object);
- }
- }
-
- void Evaluate(const WorkspaceSet &workspaces, const OBJ &object, ARGS... args,
- FeatureVector *result) const override {
- IDX focus =
- static_cast<const DER *>(this)->GetFocus(workspaces, object, args...);
- for (auto *function : this->nested()) {
- function->Evaluate(workspaces, object, focus, args..., result);
- }
- }
-
- // Returns the first nested feature's computed value.
- FeatureValue Compute(const WorkspaceSet &workspaces, const OBJ &object,
- ARGS... args,
- const FeatureVector *result) const override {
- IDX focus =
- static_cast<const DER *>(this)->GetFocus(workspaces, object, args...);
- return this->nested()[0]->Compute(workspaces, object, focus, args...,
- result);
- }
-};
-
-// CRTP feature locator class. This is a meta feature that modifies ARGS and
-// then calls the nested feature functions with the modified ARGS. Note that in
-// order for this template to work correctly, all of ARGS must be types for
-// which the reference operator & can be interpreted as a pointer to the
-// argument. The derived class DER must implement the UpdateFocus method which
-// takes pointers to the ARGS arguments:
-//
-// // Updates the current arguments.
-// void UpdateArgs(const OBJ &object, ARGS *...args) const;
-template <class DER, class OBJ, class... ARGS>
-class FeatureLocator : public MetaFeatureFunction<OBJ, ARGS...> {
- public:
- // Feature locators have an additional check that there is no intrinsic type,
- // but only in debug mode: having an intrinsic type here is odd, but not
- // enough to motive a crash in prod.
- void GetFeatureTypes(std::vector<FeatureType *> *types) const override {
- TC_DCHECK_EQ(this->feature_type(), nullptr)
- << "FeatureLocators should not have an intrinsic type.";
- MetaFeatureFunction<OBJ, ARGS...>::GetFeatureTypes(types);
- }
-
- // Evaluates the locator.
- void Evaluate(const WorkspaceSet &workspaces, const OBJ &object, ARGS... args,
- FeatureVector *result) const override {
- static_cast<const DER *>(this)->UpdateArgs(workspaces, object, &args...);
- for (auto *function : this->nested()) {
- function->Evaluate(workspaces, object, args..., result);
- }
- }
-
- // Returns the first nested feature's computed value.
- FeatureValue Compute(const WorkspaceSet &workspaces, const OBJ &object,
- ARGS... args,
- const FeatureVector *result) const override {
- static_cast<const DER *>(this)->UpdateArgs(workspaces, object, &args...);
- return this->nested()[0]->Compute(workspaces, object, args..., result);
- }
-};
-
-// Feature extractor for extracting features from objects of a certain class.
-// Template type parameters are as defined for FeatureFunction.
-template <class OBJ, class... ARGS>
-class FeatureExtractor : public GenericFeatureExtractor {
- public:
- // Feature function type for top-level functions in the feature extractor.
- typedef FeatureFunction<OBJ, ARGS...> Function;
- typedef FeatureExtractor<OBJ, ARGS...> Self;
-
- // Feature locator type for the feature extractor.
- template <class DER>
- using Locator = FeatureLocator<DER, OBJ, ARGS...>;
-
- // Initializes feature extractor.
- FeatureExtractor() {}
-
- ~FeatureExtractor() override {
- // Fully qualified class name, to avoid an ambiguity error when building for
- // Android.
- ::libtextclassifier::STLDeleteElements(&functions_);
- }
-
- // Sets up the feature extractor. Note that only top-level functions exist
- // until Setup() is called. This does not take ownership over the context,
- // which must outlive this.
- bool Setup(TaskContext *context) {
- for (Function *function : functions_) {
- if (!function->Setup(context)) return false;
- }
- return true;
- }
-
- // Initializes the feature extractor. Must be called after Setup(). This
- // does not take ownership over the context, which must outlive this.
- bool Init(TaskContext *context) {
- for (Function *function : functions_) {
- if (!function->Init(context)) return false;
- }
- if (!this->InitializeFeatureTypes()) {
- return false;
- }
- return true;
- }
-
- // Requests workspaces from the registry. Must be called after Init(), and
- // before Preprocess(). Does not take ownership over registry. This should be
- // the same registry used to initialize the WorkspaceSet used in Preprocess()
- // and ExtractFeatures(). NB: This is a different ordering from that used in
- // SentenceFeatureRepresentation style feature computation.
- void RequestWorkspaces(WorkspaceRegistry *registry) {
- for (auto *function : functions_) function->RequestWorkspaces(registry);
- }
-
- // Preprocesses the object using feature functions for the phase. Must be
- // called before any calls to ExtractFeatures() on that object and phase.
- void Preprocess(WorkspaceSet *workspaces, OBJ *object) const {
- for (Function *function : functions_) {
- function->Preprocess(workspaces, object);
- }
- }
-
- // Extracts features from an object with a focus. This invokes all the
- // top-level feature functions in the feature extractor. Only feature
- // functions belonging to the specified phase are invoked.
- void ExtractFeatures(const WorkspaceSet &workspaces, const OBJ &object,
- ARGS... args, FeatureVector *result) const {
- result->reserve(this->feature_types());
-
- // Extract features.
- for (int i = 0; i < functions_.size(); ++i) {
- functions_[i]->Evaluate(workspaces, object, args..., result);
- }
- }
-
- private:
- // Creates and initializes all feature functions in the feature extractor.
- bool InitializeFeatureFunctions() override {
- // Create all top-level feature functions.
- for (int i = 0; i < descriptor().feature_size(); ++i) {
- FeatureFunctionDescriptor *fd = mutable_descriptor()->mutable_feature(i);
- Function *function = Function::Instantiate(this, fd, "");
- if (function == nullptr) return false;
- functions_.push_back(function);
- }
- return true;
- }
-
- // Collect all feature types used in the feature extractor.
- void GetFeatureTypes(std::vector<FeatureType *> *types) const override {
- for (Function *function : functions_) {
- function->GetFeatureTypes(types);
- }
- }
-
- // Top-level feature functions (and variables) in the feature extractor.
- // Owned. INVARIANT: contains only non-null pointers.
- std::vector<Function *> functions_;
-};
-
-#define REGISTER_FEATURE_FUNCTION(base, name, component) \
- REGISTER_CLASS_COMPONENT(base, name, component)
-
-} // namespace nlp_core
-} // namespace libtextclassifier
-
-#endif // LIBTEXTCLASSIFIER_COMMON_FEATURE_EXTRACTOR_H_
diff --git a/common/feature-types.h b/common/feature-types.h
deleted file mode 100644
index 92814d9..0000000
--- a/common/feature-types.h
+++ /dev/null
@@ -1,189 +0,0 @@
-/*
- * Copyright (C) 2017 The Android Open Source Project
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-// Common feature types for parser components.
-
-#ifndef LIBTEXTCLASSIFIER_COMMON_FEATURE_TYPES_H_
-#define LIBTEXTCLASSIFIER_COMMON_FEATURE_TYPES_H_
-
-#include <algorithm>
-#include <map>
-#include <string>
-#include <utility>
-
-#include "util/base/integral_types.h"
-#include "util/base/logging.h"
-#include "util/strings/numbers.h"
-
-namespace libtextclassifier {
-namespace nlp_core {
-
-// TODO(djweiss) Clean this up as well.
-// Use the same type for feature values as is used for predicated.
-typedef int64 Predicate;
-typedef Predicate FeatureValue;
-
-// Each feature value in a feature vector has a feature type. The feature type
-// is used for converting feature type and value pairs to predicate values. The
-// feature type can also return names for feature values and calculate the size
-// of the feature value domain. The FeatureType class is abstract and must be
-// specialized for the concrete feature types.
-class FeatureType {
- public:
- // Initializes a feature type.
- explicit FeatureType(const std::string &name)
- : name_(name), base_(0),
- is_continuous_(name.find("continuous") != std::string::npos) {
- }
-
- virtual ~FeatureType() {}
-
- // Converts a feature value to a name.
- virtual std::string GetFeatureValueName(FeatureValue value) const = 0;
-
- // Returns the size of the feature values domain.
- virtual int64 GetDomainSize() const = 0;
-
- // Returns the feature type name.
- const std::string &name() const { return name_; }
-
- Predicate base() const { return base_; }
- void set_base(Predicate base) { base_ = base; }
-
- // Returns true iff this feature is continuous; see FloatFeatureValue.
- bool is_continuous() const { return is_continuous_; }
-
- private:
- // Feature type name.
- std::string name_;
-
- // "Base" feature value: i.e. a "slot" in a global ordering of features.
- Predicate base_;
-
- // See doc for is_continuous().
- bool is_continuous_;
-};
-
-// Feature type that is defined using an explicit map from FeatureValue to
-// std::string values. This can reduce some of the boilerplate when defining
-// features that generate enum values. Example usage:
-//
-// class BeverageSizeFeature : public FeatureFunction<Beverage>
-// enum FeatureValue { SMALL, MEDIUM, LARGE }; // values for this feature
-// void Init(TaskContext *context) override {
-// set_feature_type(new EnumFeatureType("beverage_size",
-// {{SMALL, "SMALL"}, {MEDIUM, "MEDIUM"}, {LARGE, "LARGE"}});
-// }
-// [...]
-// };
-class EnumFeatureType : public FeatureType {
- public:
- EnumFeatureType(const std::string &name,
- const std::map<FeatureValue, std::string> &value_names)
- : FeatureType(name), value_names_(value_names) {
- for (const auto &pair : value_names) {
- TC_DCHECK_GE(pair.first, 0)
- << "Invalid feature value: " << pair.first << ", " << pair.second;
- domain_size_ = std::max(domain_size_, pair.first + 1);
- }
- }
-
- // Returns the feature name for a given feature value.
- std::string GetFeatureValueName(FeatureValue value) const override {
- auto it = value_names_.find(value);
- if (it == value_names_.end()) {
- TC_LOG(ERROR) << "Invalid feature value " << value << " for " << name();
- return "<INVALID>";
- }
- return it->second;
- }
-
- // Returns the number of possible values for this feature type. This is one
- // greater than the largest value in the value_names map.
- FeatureValue GetDomainSize() const override { return domain_size_; }
-
- protected:
- // Maximum possible value this feature could take.
- FeatureValue domain_size_ = 0;
-
- // Names of feature values.
- std::map<FeatureValue, std::string> value_names_;
-};
-
-// Feature type for binary features.
-class BinaryFeatureType : public FeatureType {
- public:
- BinaryFeatureType(const std::string &name, const std::string &off,
- const std::string &on)
- : FeatureType(name), off_(off), on_(on) {}
-
- // Returns the feature name for a given feature value.
- std::string GetFeatureValueName(FeatureValue value) const override {
- if (value == 0) return off_;
- if (value == 1) return on_;
- return "";
- }
-
- // Binary features always have two feature values.
- FeatureValue GetDomainSize() const override { return 2; }
-
- private:
- // Feature value names for on and off.
- std::string off_;
- std::string on_;
-};
-
-// Feature type for numeric features.
-class NumericFeatureType : public FeatureType {
- public:
- // Initializes numeric feature.
- NumericFeatureType(const std::string &name, FeatureValue size)
- : FeatureType(name), size_(size) {}
-
- // Returns numeric feature value.
- std::string GetFeatureValueName(FeatureValue value) const override {
- if (value < 0) return "";
- return IntToString(value);
- }
-
- // Returns the number of feature values.
- FeatureValue GetDomainSize() const override { return size_; }
-
- private:
- // The underlying size of the numeric feature.
- FeatureValue size_;
-};
-
-// Feature type for byte features, including an "outside" value.
-class ByteFeatureType : public NumericFeatureType {
- public:
- explicit ByteFeatureType(const std::string &name)
- : NumericFeatureType(name, 257) {}
-
- std::string GetFeatureValueName(FeatureValue value) const override {
- if (value == 256) {
- return "<NULL>";
- }
- std::string result;
- result += static_cast<char>(value);
- return result;
- }
-};
-
-} // namespace nlp_core
-} // namespace libtextclassifier
-
-#endif // LIBTEXTCLASSIFIER_COMMON_FEATURE_TYPES_H_
diff --git a/common/file-utils.cc b/common/file-utils.cc
deleted file mode 100644
index 6ae4442..0000000
--- a/common/file-utils.cc
+++ /dev/null
@@ -1,76 +0,0 @@
-/*
- * Copyright (C) 2017 The Android Open Source Project
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#include "common/file-utils.h"
-
-#include <fcntl.h>
-#include <stdio.h>
-#include <sys/stat.h>
-#include <sys/types.h>
-
-#include <fstream>
-#include <memory>
-#include <string>
-
-#include "util/base/logging.h"
-
-namespace libtextclassifier {
-namespace nlp_core {
-
-namespace file_utils {
-
-bool GetFileContent(const std::string &filename, std::string *content) {
- std::ifstream input_stream(filename, std::ifstream::binary);
- if (input_stream.fail()) {
- TC_LOG(INFO) << "Error opening " << filename;
- return false;
- }
-
- content->assign(
- std::istreambuf_iterator<char>(input_stream),
- std::istreambuf_iterator<char>());
-
- if (input_stream.fail()) {
- TC_LOG(ERROR) << "Error reading " << filename;
- return false;
- }
-
- TC_LOG(INFO) << "Successfully read " << filename;
- return true;
-}
-
-bool FileExists(const std::string &filename) {
- struct stat s = {0};
- if (!stat(filename.c_str(), &s)) {
- return s.st_mode & S_IFREG;
- } else {
- return false;
- }
-}
-
-bool DirectoryExists(const std::string &dirpath) {
- struct stat s = {0};
- if (!stat(dirpath.c_str(), &s)) {
- return s.st_mode & S_IFDIR;
- } else {
- return false;
- }
-}
-
-} // namespace file_utils
-
-} // namespace nlp_core
-} // namespace libtextclassifier
diff --git a/common/file-utils.h b/common/file-utils.h
deleted file mode 100644
index e2a60f2..0000000
--- a/common/file-utils.h
+++ /dev/null
@@ -1,91 +0,0 @@
-/*
- * Copyright (C) 2017 The Android Open Source Project
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#ifndef LIBTEXTCLASSIFIER_COMMON_FILE_UTILS_H_
-#define LIBTEXTCLASSIFIER_COMMON_FILE_UTILS_H_
-
-#include <cstddef>
-#include <memory>
-#include <string>
-
-#include "common/config.h"
-
-#if PORTABLE_SAFT_MOBILE
-#include <google/protobuf/io/zero_copy_stream_impl_lite.h>
-#endif
-
-#include "common/mmap.h"
-#include "util/strings/stringpiece.h"
-
-namespace libtextclassifier {
-namespace nlp_core {
-
-namespace file_utils {
-
-// Reads the entire content of a file into a string. Returns true on success,
-// false on error.
-bool GetFileContent(const std::string &filename, std::string *content);
-
-// Parses a proto from its serialized representation in memory. That
-// representation starts at address sp.data() and contains exactly sp.size()
-// bytes. Returns true on success, false otherwise.
-template<class Proto>
-bool ParseProtoFromMemory(StringPiece sp, Proto *proto) {
- if (!sp.data()) {
- // Avoid passing a nullptr to ArrayInputStream below.
- return false;
- }
-#if PORTABLE_SAFT_MOBILE
- ::google::protobuf::io::ArrayInputStream stream(sp.data(), sp.size());
- return proto->ParseFromZeroCopyStream(&stream);
-#else
-
- std::string data(sp.data(), sp.size());
- return proto->ParseFromString(data);
-#endif
-}
-
-// Parses a proto from a file. Returns true on success, false otherwise.
-//
-// Note: the entire content of the file should be the binary (not
-// human-readable) serialization of a protocol buffer.
-//
-// Note: when we compile for Android, the proto parsing methods need to know the
-// type of the message they are parsing. We use template polymorphism for that.
-template<class Proto>
-bool ReadProtoFromFile(const std::string &filename, Proto *proto) {
- ScopedMmap scoped_mmap(filename);
- const MmapHandle &handle = scoped_mmap.handle();
- if (!handle.ok()) {
- return false;
- }
- return ParseProtoFromMemory(handle.to_stringpiece(), proto);
-}
-
-// Returns true if filename is the name of an existing file, and false
-// otherwise.
-bool FileExists(const std::string &filename);
-
-// Returns true if dirpath is the path to an existing directory, and false
-// otherwise.
-bool DirectoryExists(const std::string &dirpath);
-
-} // namespace file_utils
-
-} // namespace nlp_core
-} // namespace libtextclassifier
-
-#endif // LIBTEXTCLASSIFIER_COMMON_FILE_UTILS_H_
diff --git a/common/float16.h b/common/float16.h
deleted file mode 100644
index 8b52be3..0000000
--- a/common/float16.h
+++ /dev/null
@@ -1,64 +0,0 @@
-/*
- * Copyright (C) 2017 The Android Open Source Project
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#ifndef LIBTEXTCLASSIFIER_COMMON_FLOAT16_H_
-#define LIBTEXTCLASSIFIER_COMMON_FLOAT16_H_
-
-#include "util/base/casts.h"
-#include "util/base/integral_types.h"
-
-namespace libtextclassifier {
-namespace nlp_core {
-
-// 16 bit encoding of a float. NOTE: can't be used directly for computation:
-// one first needs to convert it to a normal float, using Float16To32.
-//
-// Documentation copied from original file:
-//
-// Compact 16-bit encoding of floating point numbers. This
-// representation uses 1 bit for the sign, 8 bits for the exponent and
-// 7 bits for the mantissa. It is assumed that floats are in IEEE 754
-// format so a float16 is just bits 16-31 of a single precision float.
-//
-// NOTE: The IEEE floating point standard defines a float16 format that
-// is different than this format (it has fewer bits of exponent and more
-// bits of mantissa). We don't use that format here because conversion
-// to/from 32-bit floats is more complex for that format, and the
-// conversion for this format is very simple.
-//
-// <---------float16------------>
-// s e e e e e e e e f f f f f f f f f f f f f f f f f f f f f f f
-// <------------------------------float-------------------------->
-// 3 3 2 2 1 1 0
-// 1 0 3 2 5 4 0
-
-typedef uint16 float16;
-
-static inline float16 Float32To16(float f) {
- // Note that we just truncate the mantissa bits: we make no effort to
- // do any smarter rounding.
- return (bit_cast<uint32>(f) >> 16) & 0xffff;
-}
-
-static inline float Float16To32(float16 f) {
- // We fill in the new mantissa bits with 0, and don't do anything smarter.
- return bit_cast<float>(f << 16);
-}
-
-} // namespace nlp_core
-} // namespace libtextclassifier
-
-#endif // LIBTEXTCLASSIFIER_COMMON_FLOAT16_H_
diff --git a/common/fml-parser.cc b/common/fml-parser.cc
deleted file mode 100644
index 2964671..0000000
--- a/common/fml-parser.cc
+++ /dev/null
@@ -1,329 +0,0 @@
-/*
- * Copyright (C) 2017 The Android Open Source Project
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#include "common/fml-parser.h"
-
-#include <ctype.h>
-#include <string>
-
-#include "util/base/logging.h"
-#include "util/strings/numbers.h"
-
-namespace libtextclassifier {
-namespace nlp_core {
-
-namespace {
-inline bool IsValidCharAtStartOfIdentifier(char c) {
- return isalpha(c) || (c == '_') || (c == '/');
-}
-
-// Returns true iff character c can appear inside an identifier.
-inline bool IsValidCharInsideIdentifier(char c) {
- return isalnum(c) || (c == '_') || (c == '-') || (c == '/');
-}
-
-// Returns true iff character c can appear at the beginning of a number.
-inline bool IsValidCharAtStartOfNumber(char c) {
- return isdigit(c) || (c == '+') || (c == '-');
-}
-
-// Returns true iff character c can appear inside a number.
-inline bool IsValidCharInsideNumber(char c) {
- return isdigit(c) || (c == '.');
-}
-} // namespace
-
-bool FMLParser::Initialize(const std::string &source) {
- // Initialize parser state.
- source_ = source;
- current_ = source_.begin();
- item_start_ = line_start_ = current_;
- line_number_ = item_line_number_ = 1;
-
- // Read first input item.
- return NextItem();
-}
-
-void FMLParser::ReportError(const std::string &error_message) {
- const int position = item_start_ - line_start_ + 1;
- const std::string line(line_start_, current_);
-
- TC_LOG(ERROR) << "Error in feature model, line " << item_line_number_
- << ", position " << position << ": " << error_message
- << "\n " << line << " <--HERE";
-}
-
-void FMLParser::Next() {
- // Move to the next input character. If we are at a line break update line
- // number and line start position.
- if (CurrentChar() == '\n') {
- ++line_number_;
- ++current_;
- line_start_ = current_;
- } else {
- ++current_;
- }
-}
-
-bool FMLParser::NextItem() {
- // Skip white space and comments.
- while (!eos()) {
- if (CurrentChar() == '#') {
- // Skip comment.
- while (!eos() && CurrentChar() != '\n') Next();
- } else if (isspace(CurrentChar())) {
- // Skip whitespace.
- while (!eos() && isspace(CurrentChar())) Next();
- } else {
- break;
- }
- }
-
- // Record start position for next item.
- item_start_ = current_;
- item_line_number_ = line_number_;
-
- // Check for end of input.
- if (eos()) {
- item_type_ = END;
- return true;
- }
-
- // Parse number.
- if (IsValidCharAtStartOfNumber(CurrentChar())) {
- std::string::iterator start = current_;
- Next();
- while (!eos() && IsValidCharInsideNumber(CurrentChar())) Next();
- item_text_.assign(start, current_);
- item_type_ = NUMBER;
- return true;
- }
-
- // Parse std::string.
- if (CurrentChar() == '"') {
- Next();
- std::string::iterator start = current_;
- while (CurrentChar() != '"') {
- if (eos()) {
- ReportError("Unterminated string");
- return false;
- }
- Next();
- }
- item_text_.assign(start, current_);
- item_type_ = STRING;
- Next();
- return true;
- }
-
- // Parse identifier name.
- if (IsValidCharAtStartOfIdentifier(CurrentChar())) {
- std::string::iterator start = current_;
- while (!eos() && IsValidCharInsideIdentifier(CurrentChar())) {
- Next();
- }
- item_text_.assign(start, current_);
- item_type_ = NAME;
- return true;
- }
-
- // Single character item.
- item_type_ = CurrentChar();
- Next();
- return true;
-}
-
-bool FMLParser::Parse(const std::string &source,
- FeatureExtractorDescriptor *result) {
- // Initialize parser.
- if (!Initialize(source)) {
- return false;
- }
-
- while (item_type_ != END) {
- // Current item should be a feature name.
- if (item_type_ != NAME) {
- ReportError("Feature type name expected");
- return false;
- }
- std::string name = item_text_;
- if (!NextItem()) {
- return false;
- }
-
- // Parse feature.
- FeatureFunctionDescriptor *descriptor = result->add_feature();
- descriptor->set_type(name);
- if (!ParseFeature(descriptor)) {
- return false;
- }
- }
-
- return true;
-}
-
-bool FMLParser::ParseFeature(FeatureFunctionDescriptor *result) {
- // Parse argument and parameters.
- if (item_type_ == '(') {
- if (!NextItem()) return false;
- if (!ParseParameter(result)) return false;
- while (item_type_ == ',') {
- if (!NextItem()) return false;
- if (!ParseParameter(result)) return false;
- }
-
- if (item_type_ != ')') {
- ReportError(") expected");
- return false;
- }
- if (!NextItem()) return false;
- }
-
- // Parse feature name.
- if (item_type_ == ':') {
- if (!NextItem()) return false;
- if (item_type_ != NAME && item_type_ != STRING) {
- ReportError("Feature name expected");
- return false;
- }
- std::string name = item_text_;
- if (!NextItem()) return false;
-
- // Set feature name.
- result->set_name(name);
- }
-
- // Parse sub-features.
- if (item_type_ == '.') {
- // Parse dotted sub-feature.
- if (!NextItem()) return false;
- if (item_type_ != NAME) {
- ReportError("Feature type name expected");
- return false;
- }
- std::string type = item_text_;
- if (!NextItem()) return false;
-
- // Parse sub-feature.
- FeatureFunctionDescriptor *subfeature = result->add_feature();
- subfeature->set_type(type);
- if (!ParseFeature(subfeature)) return false;
- } else if (item_type_ == '{') {
- // Parse sub-feature block.
- if (!NextItem()) return false;
- while (item_type_ != '}') {
- if (item_type_ != NAME) {
- ReportError("Feature type name expected");
- return false;
- }
- std::string type = item_text_;
- if (!NextItem()) return false;
-
- // Parse sub-feature.
- FeatureFunctionDescriptor *subfeature = result->add_feature();
- subfeature->set_type(type);
- if (!ParseFeature(subfeature)) return false;
- }
- if (!NextItem()) return false;
- }
- return true;
-}
-
-bool FMLParser::ParseParameter(FeatureFunctionDescriptor *result) {
- if (item_type_ == NUMBER) {
- int32 argument;
- if (!ParseInt32(item_text_.c_str(), &argument)) {
- ReportError("Unable to parse number");
- return false;
- }
- if (!NextItem()) return false;
-
- // Set default argument for feature.
- result->set_argument(argument);
- } else if (item_type_ == NAME) {
- std::string name = item_text_;
- if (!NextItem()) return false;
- if (item_type_ != '=') {
- ReportError("= expected");
- return false;
- }
- if (!NextItem()) return false;
- if (item_type_ >= END) {
- ReportError("Parameter value expected");
- return false;
- }
- std::string value = item_text_;
- if (!NextItem()) return false;
-
- // Add parameter to feature.
- Parameter *parameter;
- parameter = result->add_parameter();
- parameter->set_name(name);
- parameter->set_value(value);
- } else {
- ReportError("Syntax error in parameter list");
- return false;
- }
- return true;
-}
-
-void ToFMLFunction(const FeatureFunctionDescriptor &function,
- std::string *output) {
- output->append(function.type());
- if (function.argument() != 0 || function.parameter_size() > 0) {
- output->append("(");
- bool first = true;
- if (function.argument() != 0) {
- output->append(IntToString(function.argument()));
- first = false;
- }
- for (int i = 0; i < function.parameter_size(); ++i) {
- if (!first) output->append(",");
- output->append(function.parameter(i).name());
- output->append("=");
- output->append("\"");
- output->append(function.parameter(i).value());
- output->append("\"");
- first = false;
- }
- output->append(")");
- }
-}
-
-void ToFML(const FeatureFunctionDescriptor &function, std::string *output) {
- ToFMLFunction(function, output);
- if (function.feature_size() == 1) {
- output->append(".");
- ToFML(function.feature(0), output);
- } else if (function.feature_size() > 1) {
- output->append(" { ");
- for (int i = 0; i < function.feature_size(); ++i) {
- if (i > 0) output->append(" ");
- ToFML(function.feature(i), output);
- }
- output->append(" } ");
- }
-}
-
-void ToFML(const FeatureExtractorDescriptor &extractor, std::string *output) {
- for (int i = 0; i < extractor.feature_size(); ++i) {
- ToFML(extractor.feature(i), output);
- output->append("\n");
- }
-}
-
-} // namespace nlp_core
-} // namespace libtextclassifier
diff --git a/common/fml-parser.h b/common/fml-parser.h
deleted file mode 100644
index b6b9da2..0000000
--- a/common/fml-parser.h
+++ /dev/null
@@ -1,147 +0,0 @@
-/*
- * Copyright (C) 2017 The Android Open Source Project
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-// Feature modeling language (fml) parser.
-//
-// BNF grammar for fml:
-//
-// <feature model> ::= { <feature extractor> }
-//
-// <feature extractor> ::= <extractor spec> |
-// <extractor spec> '.' <feature extractor> |
-// <extractor spec> '{' { <feature extractor> } '}'
-//
-// <extractor spec> ::= <extractor type>
-// [ '(' <parameter list> ')' ]
-// [ ':' <extractor name> ]
-//
-// <parameter list> = ( <parameter> | <argument> ) { ',' <parameter> }
-//
-// <parameter> ::= <parameter name> '=' <parameter value>
-//
-// <extractor type> ::= NAME
-// <extractor name> ::= NAME | STRING
-// <argument> ::= NUMBER
-// <parameter name> ::= NAME
-// <parameter value> ::= NUMBER | STRING | NAME
-
-#ifndef LIBTEXTCLASSIFIER_COMMON_FML_PARSER_H_
-#define LIBTEXTCLASSIFIER_COMMON_FML_PARSER_H_
-
-#include <string>
-#include <vector>
-
-#include "common/feature-descriptors.h"
-#include "util/base/logging.h"
-
-namespace libtextclassifier {
-namespace nlp_core {
-
-class FMLParser {
- public:
- // Parses fml specification into feature extractor descriptor.
- // Returns true on success, false on error (e.g., syntax errors).
- bool Parse(const std::string &source, FeatureExtractorDescriptor *result);
-
- private:
- // Initializes the parser with the source text.
- // Returns true on success, false on syntax error.
- bool Initialize(const std::string &source);
-
- // Outputs an error message, with context info, and sets error_ to true.
- void ReportError(const std::string &error_message);
-
- // Moves to the next input character.
- void Next();
-
- // Moves to the next input item. Sets item_text_ and item_type_ accordingly.
- // Returns true on success, false on syntax error.
- bool NextItem();
-
- // Parses a feature descriptor.
- // Returns true on success, false on syntax error.
- bool ParseFeature(FeatureFunctionDescriptor *result);
-
- // Parses a parameter specification.
- // Returns true on success, false on syntax error.
- bool ParseParameter(FeatureFunctionDescriptor *result);
-
- // Returns true if end of source input has been reached.
- bool eos() const { return current_ >= source_.end(); }
-
- // Returns current character. Other methods should access the current
- // character through this method (instead of using *current_ directly): this
- // method performs extra safety checks.
- //
- // In case of an unsafe access, returns '\0'.
- char CurrentChar() const {
- if ((current_ >= source_.begin()) && (current_ < source_.end())) {
- return *current_;
- } else {
- TC_LOG(ERROR) << "Unsafe char read";
- return '\0';
- }
- }
-
- // Item types.
- enum ItemTypes {
- END = 0,
- NAME = -1,
- NUMBER = -2,
- STRING = -3,
- };
-
- // Source text.
- std::string source_;
-
- // Current input position.
- std::string::iterator current_;
-
- // Line number for current input position.
- int line_number_;
-
- // Start position for current item.
- std::string::iterator item_start_;
-
- // Start position for current line.
- std::string::iterator line_start_;
-
- // Line number for current item.
- int item_line_number_;
-
- // Item type for current item. If this is positive it is interpreted as a
- // character. If it is negative it is interpreted as an item type.
- int item_type_;
-
- // Text for current item.
- std::string item_text_;
-};
-
-// Converts a FeatureFunctionDescriptor into an FML spec (reverse of parsing).
-void ToFML(const FeatureFunctionDescriptor &function, std::string *output);
-
-// Like ToFML, but doesn't go into the nested functions. Instead, it generates
-// a string that starts with the name of the feature extraction function and
-// next, in-between parentheses, the parameters, separated by comma.
-// Intuitively, the constructed string is the prefix of ToFML, before the "{"
-// that starts the nested features.
-void ToFMLFunction(const FeatureFunctionDescriptor &function,
- std::string *output);
-
-} // namespace nlp_core
-} // namespace libtextclassifier
-
-#endif // LIBTEXTCLASSIFIER_COMMON_FML_PARSER_H_
diff --git a/common/fml-parser_test.cc b/common/fml-parser_test.cc
deleted file mode 100644
index b46048f..0000000
--- a/common/fml-parser_test.cc
+++ /dev/null
@@ -1,157 +0,0 @@
-/*
- * Copyright (C) 2017 The Android Open Source Project
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#include "common/fml-parser.h"
-
-#include "common/feature-descriptors.h"
-#include "gtest/gtest.h"
-
-namespace libtextclassifier {
-namespace nlp_core {
-
-TEST(FMLParserTest, NoFeature) {
- FMLParser fml_parser;
- FeatureExtractorDescriptor descriptor;
- const std::string kFeatureName = "";
- EXPECT_TRUE(fml_parser.Parse(kFeatureName, &descriptor));
- EXPECT_EQ(0, descriptor.feature_size());
-}
-
-TEST(FMLParserTest, FeatureWithNoParams) {
- FMLParser fml_parser;
- FeatureExtractorDescriptor descriptor;
- const std::string kFeatureName = "continuous-bag-of-relevant-scripts";
- EXPECT_TRUE(fml_parser.Parse(kFeatureName, &descriptor));
- EXPECT_EQ(1, descriptor.feature_size());
- EXPECT_EQ(kFeatureName, descriptor.feature(0).type());
-}
-
-TEST(FMLParserTest, FeatureWithOneKeywordParameter) {
- FMLParser fml_parser;
- FeatureExtractorDescriptor descriptor;
- EXPECT_TRUE(fml_parser.Parse("myfeature(start=2)", &descriptor));
- EXPECT_EQ(1, descriptor.feature_size());
- EXPECT_EQ("myfeature", descriptor.feature(0).type());
- EXPECT_EQ(1, descriptor.feature(0).parameter_size());
- EXPECT_EQ("start", descriptor.feature(0).parameter(0).name());
- EXPECT_EQ("2", descriptor.feature(0).parameter(0).value());
- EXPECT_FALSE(descriptor.feature(0).has_argument());
-}
-
-TEST(FMLParserTest, FeatureWithDefaultArgumentNegative) {
- FMLParser fml_parser;
- FeatureExtractorDescriptor descriptor;
- EXPECT_TRUE(fml_parser.Parse("offset(-3)", &descriptor));
- EXPECT_EQ(1, descriptor.feature_size());
- EXPECT_EQ("offset", descriptor.feature(0).type());
- EXPECT_EQ(0, descriptor.feature(0).parameter_size());
- EXPECT_EQ(-3, descriptor.feature(0).argument());
-}
-
-TEST(FMLParserTest, FeatureWithDefaultArgumentPositive) {
- FMLParser fml_parser;
- FeatureExtractorDescriptor descriptor;
- EXPECT_TRUE(fml_parser.Parse("delta(7)", &descriptor));
- EXPECT_EQ(1, descriptor.feature_size());
- EXPECT_EQ("delta", descriptor.feature(0).type());
- EXPECT_EQ(0, descriptor.feature(0).parameter_size());
- EXPECT_EQ(7, descriptor.feature(0).argument());
-}
-
-TEST(FMLParserTest, FeatureWithDefaultArgumentZero) {
- FMLParser fml_parser;
- FeatureExtractorDescriptor descriptor;
- EXPECT_TRUE(fml_parser.Parse("delta(0)", &descriptor));
- EXPECT_EQ(1, descriptor.feature_size());
- EXPECT_EQ("delta", descriptor.feature(0).type());
- EXPECT_EQ(0, descriptor.feature(0).parameter_size());
- EXPECT_EQ(0, descriptor.feature(0).argument());
-}
-
-TEST(FMLParserTest, FeatureWithManyKeywordParameters) {
- FMLParser fml_parser;
- FeatureExtractorDescriptor descriptor;
- EXPECT_TRUE(fml_parser.Parse("myfeature(ratio=0.316,start=2,name=\"foo\")",
- &descriptor));
- EXPECT_EQ(1, descriptor.feature_size());
- EXPECT_EQ("myfeature", descriptor.feature(0).type());
- EXPECT_EQ(3, descriptor.feature(0).parameter_size());
- EXPECT_EQ("ratio", descriptor.feature(0).parameter(0).name());
- EXPECT_EQ("0.316", descriptor.feature(0).parameter(0).value());
- EXPECT_EQ("start", descriptor.feature(0).parameter(1).name());
- EXPECT_EQ("2", descriptor.feature(0).parameter(1).value());
- EXPECT_EQ("name", descriptor.feature(0).parameter(2).name());
- EXPECT_EQ("foo", descriptor.feature(0).parameter(2).value());
- EXPECT_FALSE(descriptor.feature(0).has_argument());
-}
-
-TEST(FMLParserTest, FeatureWithAllKindsOfParameters) {
- FMLParser fml_parser;
- FeatureExtractorDescriptor descriptor;
- EXPECT_TRUE(
- fml_parser.Parse("myfeature(17,ratio=0.316,start=2)", &descriptor));
- EXPECT_EQ(1, descriptor.feature_size());
- EXPECT_EQ("myfeature", descriptor.feature(0).type());
- EXPECT_EQ(2, descriptor.feature(0).parameter_size());
- EXPECT_EQ("ratio", descriptor.feature(0).parameter(0).name());
- EXPECT_EQ("0.316", descriptor.feature(0).parameter(0).value());
- EXPECT_EQ("start", descriptor.feature(0).parameter(1).name());
- EXPECT_EQ("2", descriptor.feature(0).parameter(1).value());
- EXPECT_EQ(17, descriptor.feature(0).argument());
-}
-
-TEST(FMLParserTest, FeatureWithWhitespaces) {
- FMLParser fml_parser;
- FeatureExtractorDescriptor descriptor;
- EXPECT_TRUE(fml_parser.Parse(
- " myfeature\t\t\t\n(17,\nratio=0.316 , start=2) ", &descriptor));
- EXPECT_EQ(1, descriptor.feature_size());
- EXPECT_EQ("myfeature", descriptor.feature(0).type());
- EXPECT_EQ(2, descriptor.feature(0).parameter_size());
- EXPECT_EQ("ratio", descriptor.feature(0).parameter(0).name());
- EXPECT_EQ("0.316", descriptor.feature(0).parameter(0).value());
- EXPECT_EQ("start", descriptor.feature(0).parameter(1).name());
- EXPECT_EQ("2", descriptor.feature(0).parameter(1).value());
- EXPECT_EQ(17, descriptor.feature(0).argument());
-}
-
-TEST(FMLParserTest, Broken_ParamWithoutValue) {
- FMLParser fml_parser;
- FeatureExtractorDescriptor descriptor;
- EXPECT_FALSE(
- fml_parser.Parse("myfeature(17,ratio=0.316,start)", &descriptor));
-}
-
-TEST(FMLParserTest, Broken_MissingCloseParen) {
- FMLParser fml_parser;
- FeatureExtractorDescriptor descriptor;
- EXPECT_FALSE(fml_parser.Parse("myfeature(17,ratio=0.316", &descriptor));
-}
-
-TEST(FMLParserTest, Broken_MissingOpenParen) {
- FMLParser fml_parser;
- FeatureExtractorDescriptor descriptor;
- EXPECT_FALSE(fml_parser.Parse("myfeature17,ratio=0.316)", &descriptor));
-}
-
-TEST(FMLParserTest, Broken_MissingQuote) {
- FMLParser fml_parser;
- FeatureExtractorDescriptor descriptor;
- EXPECT_FALSE(fml_parser.Parse("count(17,name=\"foo)", &descriptor));
-}
-
-} // namespace nlp_core
-} // namespace libtextclassifier
diff --git a/common/list-of-strings.proto b/common/list-of-strings.proto
deleted file mode 100644
index 5ba45ed..0000000
--- a/common/list-of-strings.proto
+++ /dev/null
@@ -1,22 +0,0 @@
-// Copyright (C) 2017 The Android Open Source Project
-//
-// Licensed under the Apache License, Version 2.0 (the "License");
-// you may not use this file except in compliance with the License.
-// You may obtain a copy of the License at
-//
-// http://www.apache.org/licenses/LICENSE-2.0
-//
-// Unless required by applicable law or agreed to in writing, software
-// distributed under the License is distributed on an "AS IS" BASIS,
-// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-// See the License for the specific language governing permissions and
-// limitations under the License.
-
-syntax = "proto2";
-option optimize_for = LITE_RUNTIME;
-
-package libtextclassifier.nlp_core;
-
-message ListOfStrings {
- repeated string element = 1;
-}
diff --git a/common/little-endian-data.h b/common/little-endian-data.h
deleted file mode 100644
index e3bc88f..0000000
--- a/common/little-endian-data.h
+++ /dev/null
@@ -1,124 +0,0 @@
-/*
- * Copyright (C) 2017 The Android Open Source Project
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#ifndef LIBTEXTCLASSIFIER_COMMON_LITTLE_ENDIAN_DATA_H_
-#define LIBTEXTCLASSIFIER_COMMON_LITTLE_ENDIAN_DATA_H_
-
-#include <algorithm>
-#include <string>
-#include <vector>
-
-#include "util/base/endian.h"
-#include "util/base/logging.h"
-
-namespace libtextclassifier {
-namespace nlp_core {
-
-// Swaps the sizeof(T) bytes that start at addr. E.g., if sizeof(T) == 2,
-// then (addr[0], addr[1]) -> (addr[1], addr[0]). Useful for little endian
-// <-> big endian conversions.
-template <class T>
-void SwapBytes(T *addr) {
- char *char_ptr = reinterpret_cast<char *>(addr);
- std::reverse(char_ptr, char_ptr + sizeof(T));
-}
-
-// Assuming addr points to a piece of data of type T, with its bytes in the
-// little/big endian order specific to the machine this code runs on, this
-// method will re-arrange the bytes (in place) in little-endian order.
-template <class T>
-void HostToLittleEndian(T *addr) {
- if (LittleEndian::IsLittleEndian()) {
- // Do nothing: current machine is little-endian.
- } else {
- SwapBytes(addr);
- }
-}
-
-// Reverse of HostToLittleEndian.
-template <class T>
-void LittleEndianToHost(T *addr) {
- // It turns out it's the same function: on little-endian machines, do nothing
- // (source and target formats are identical). Otherwise, swap bytes.
- HostToLittleEndian(addr);
-}
-
-// Returns string obtained by concatenating the bytes of the elements from a
-// vector (in order: v[0], v[1], etc). If the type T requires more than one
-// byte, the byte for each element are first converted to little-endian format.
-template<typename T>
-std::string GetDataBytesInLittleEndianOrder(const std::vector<T> &v) {
- std::string data_bytes;
- for (const T element : v) {
- T little_endian_element = element;
- HostToLittleEndian(&little_endian_element);
- data_bytes.append(
- reinterpret_cast<const char *>(&little_endian_element),
- sizeof(T));
- }
- return data_bytes;
-}
-
-// Performs reverse of GetDataBytesInLittleEndianOrder.
-//
-// I.e., decodes the data bytes from parameter bytes into num_elements Ts, and
-// places them in the vector v (previous content of that vector is erased).
-//
-// We expect bytes to contain the concatenation of the bytes for exactly
-// num_elements elements of type T. If the type T requires more than one byte,
-// those bytes should be arranged in little-endian form.
-//
-// Returns true on success and false otherwise (e.g., bytes has the wrong size).
-// Note: we do not want to crash on corrupted data (some clients, e..g, GMSCore,
-// have asked us not to do so). Instead, we report the error and let the client
-// decide what to do. On error, we also fill the vector with zeros, such that
-// at least the dimension of v matches expectations.
-template<typename T>
-bool FillVectorFromDataBytesInLittleEndian(
- const std::string &bytes, int num_elements, std::vector<T> *v) {
- if (bytes.size() != num_elements * sizeof(T)) {
- TC_LOG(ERROR) << "Wrong number of bytes: actual " << bytes.size()
- << " vs expected " << num_elements
- << " elements of sizeof(element) = " << sizeof(T)
- << " bytes each ; will fill vector with zeros";
- v->assign(num_elements, static_cast<T>(0));
- return false;
- }
- v->clear();
- v->reserve(num_elements);
- const T *start = reinterpret_cast<const T *>(bytes.data());
- if (LittleEndian::IsLittleEndian() || (sizeof(T) == 1)) {
- // Fast in the common case ([almost] all hardware today is little-endian):
- // if same endianness (or type T requires a single byte and endianness
- // irrelevant), just use the bytes.
- v->assign(start, start + num_elements);
- } else {
- // Slower (but very rare case): this code runs on a big endian machine and
- // the type T requires more than one byte. Hence, some conversion is
- // necessary.
- for (int i = 0; i < num_elements; ++i) {
- T temp = start[i];
- SwapBytes(&temp);
- v->push_back(temp);
- }
- }
- return true;
-}
-
-} // namespace nlp_core
-} // namespace libtextclassifier
-
-#endif // LIBTEXTCLASSIFIER_COMMON_LITTLE_ENDIAN_DATA_H_
diff --git a/common/memory_image/data-store.cc b/common/memory_image/data-store.cc
deleted file mode 100644
index a5f500c..0000000
--- a/common/memory_image/data-store.cc
+++ /dev/null
@@ -1,61 +0,0 @@
-/*
- * Copyright (C) 2017 The Android Open Source Project
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#include "common/memory_image/data-store.h"
-
-#include "util/base/logging.h"
-
-namespace libtextclassifier {
-namespace nlp_core {
-namespace memory_image {
-
-DataStore::DataStore(StringPiece bytes)
- : reader_(bytes.data(), bytes.size()) {
- if (!reader_.success_status()) {
- TC_LOG(ERROR) << "Unable to successfully initialize DataStore.";
- }
-}
-
-StringPiece DataStore::GetData(const std::string &name) const {
- if (!reader_.success_status()) {
- TC_LOG(ERROR) << "DataStore::GetData(" << name << ") "
- << "called on invalid DataStore; will return empty data "
- << "chunk";
- return StringPiece();
- }
-
- const auto &entries = reader_.trimmed_proto().entries();
- const auto &it = entries.find(name);
- if (it == entries.end()) {
- TC_LOG(ERROR) << "Unknown key: " << name
- << "; will return empty data chunk";
- return StringPiece();
- }
-
- const DataStoreEntryBytes &entry_bytes = it->second;
- if (!entry_bytes.has_blob_index()) {
- TC_LOG(ERROR) << "DataStoreEntryBytes with no blob_index; "
- << "will return empty data chunk.";
- return StringPiece();
- }
-
- int blob_index = entry_bytes.blob_index();
- return reader_.data_blob_view(blob_index);
-}
-
-} // namespace memory_image
-} // namespace nlp_core
-} // namespace libtextclassifier
diff --git a/common/memory_image/data-store.h b/common/memory_image/data-store.h
deleted file mode 100644
index 56aa4fc..0000000
--- a/common/memory_image/data-store.h
+++ /dev/null
@@ -1,58 +0,0 @@
-/*
- * Copyright (C) 2017 The Android Open Source Project
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#ifndef LIBTEXTCLASSIFIER_COMMON_MEMORY_IMAGE_DATA_STORE_H_
-#define LIBTEXTCLASSIFIER_COMMON_MEMORY_IMAGE_DATA_STORE_H_
-
-#include <string>
-
-#include "common/memory_image/data-store.pb.h"
-#include "common/memory_image/memory-image-reader.h"
-#include "util/strings/stringpiece.h"
-
-namespace libtextclassifier {
-namespace nlp_core {
-namespace memory_image {
-
-// Class to access a data store. See usage example in comments for
-// DataStoreBuilder.
-class DataStore {
- public:
- // Constructs a DataStore using the indicated bytes, i.e., bytes.size() bytes
- // starting at address bytes.data(). These bytes should contain the
- // serialization of a data store, see DataStoreBuilder::SerializeAsString().
- explicit DataStore(StringPiece bytes);
-
- // Retrieves (start_addr, num_bytes) info for piece of memory that contains
- // the data associated with the indicated name. Note: this piece of memory is
- // inside the [start, start + size) (see constructor). This piece of memory
- // starts at an offset from start which is a multiple of the alignment
- // specified when the data store was built using DataStoreBuilder.
- //
- // If the alignment is a low power of 2 (e..g, 4, 8, or 16) and "start" passed
- // to constructor corresponds to the beginning of a memory page or an address
- // returned by new or malloc(), then start_addr is divisible with alignment.
- StringPiece GetData(const std::string &name) const;
-
- private:
- MemoryImageReader<DataStoreProto> reader_;
-};
-
-} // namespace memory_image
-} // namespace nlp_core
-} // namespace libtextclassifier
-
-#endif // LIBTEXTCLASSIFIER_COMMON_MEMORY_IMAGE_DATA_STORE_H_
diff --git a/common/memory_image/data-store.proto b/common/memory_image/data-store.proto
deleted file mode 100644
index 68e914a..0000000
--- a/common/memory_image/data-store.proto
+++ /dev/null
@@ -1,44 +0,0 @@
-// Copyright (C) 2017 The Android Open Source Project
-//
-// Licensed under the Apache License, Version 2.0 (the "License");
-// you may not use this file except in compliance with the License.
-// You may obtain a copy of the License at
-//
-// http://www.apache.org/licenses/LICENSE-2.0
-//
-// Unless required by applicable law or agreed to in writing, software
-// distributed under the License is distributed on an "AS IS" BASIS,
-// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-// See the License for the specific language governing permissions and
-// limitations under the License.
-
-// Protos for a data store: a barebone in-memory file system.
-//
-// A DataStore maintains an association between names and chunks of bytes. It
-// can be serialized into a string. Of course, it can be deserialized from a
-// string, with minimal parsing; after deserialization, all chunks of bytes
-// start at aligned addresses (aligned = multiple of an address specified at
-// build time).
-
-syntax = "proto2";
-option optimize_for = LITE_RUNTIME;
-
-package libtextclassifier.nlp_core.memory_image;
-
-// Bytes for a data store entry. They can be stored either directly in the
-// "data" field, or in the DataBlob with the 0-based index "blob_index".
-message DataStoreEntryBytes {
- oneof data {
- // Bytes for this data store entry, stored in this message.
- string in_place_data = 1;
-
- // 0-based index of the data blob with bytes for this data store entry. In
- // this case, the actual bytes are stored outside this message; the
- // DataStore code handles the association.
- int32 blob_index = 2 [default = -1];
- }
-}
-
-message DataStoreProto {
- map<string, DataStoreEntryBytes> entries = 1;
-}
diff --git a/common/memory_image/embedding-network-params-from-image.h b/common/memory_image/embedding-network-params-from-image.h
deleted file mode 100644
index e8c7d1e..0000000
--- a/common/memory_image/embedding-network-params-from-image.h
+++ /dev/null
@@ -1,225 +0,0 @@
-/*
- * Copyright (C) 2017 The Android Open Source Project
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#ifndef LIBTEXTCLASSIFIER_COMMON_MEMORY_IMAGE_EMBEDDING_NETWORK_PARAMS_FROM_IMAGE_H_
-#define LIBTEXTCLASSIFIER_COMMON_MEMORY_IMAGE_EMBEDDING_NETWORK_PARAMS_FROM_IMAGE_H_
-
-#include "common/embedding-network-package.pb.h"
-#include "common/embedding-network-params.h"
-#include "common/embedding-network.pb.h"
-#include "common/memory_image/memory-image-reader.h"
-#include "util/base/integral_types.h"
-#include "util/strings/stringpiece.h"
-
-namespace libtextclassifier {
-namespace nlp_core {
-
-// EmbeddingNetworkParams backed by a memory image.
-//
-// In this context, a memory image is like an EmbeddingNetworkProto, but with
-// all repeated weights (>99% of the size) directly usable (with no parsing
-// required).
-class EmbeddingNetworkParamsFromImage : public EmbeddingNetworkParams {
- public:
- // Constructs an EmbeddingNetworkParamsFromImage, using the memory image that
- // starts at address start and contains num_bytes bytes.
- EmbeddingNetworkParamsFromImage(const void *start, uint64 num_bytes)
- : memory_reader_(start, num_bytes),
- trimmed_proto_(memory_reader_.trimmed_proto()) {
- embeddings_blob_offset_ = 0;
-
- hidden_blob_offset_ = embeddings_blob_offset_ + embeddings_size();
- if (trimmed_proto_.embeddings_size() &&
- trimmed_proto_.embeddings(0).is_quantized()) {
- // Adjust for quantization: each quantized matrix takes two blobs (instead
- // of one): one for the quantized values and one for the scales.
- hidden_blob_offset_ += embeddings_size();
- }
-
- hidden_bias_blob_offset_ = hidden_blob_offset_ + hidden_size();
- softmax_blob_offset_ = hidden_bias_blob_offset_ + hidden_bias_size();
- softmax_bias_blob_offset_ = softmax_blob_offset_ + softmax_size();
- }
-
- ~EmbeddingNetworkParamsFromImage() override {}
-
- const TaskSpec *GetTaskSpec() override {
- auto extension_id = task_spec_in_embedding_network_proto;
- if (trimmed_proto_.HasExtension(extension_id)) {
- return &(trimmed_proto_.GetExtension(extension_id));
- } else {
- return nullptr;
- }
- }
-
- protected:
- int embeddings_size() const override {
- return trimmed_proto_.embeddings_size();
- }
-
- int embeddings_num_rows(int i) const override {
- TC_DCHECK(InRange(i, embeddings_size()));
- return trimmed_proto_.embeddings(i).rows();
- }
-
- int embeddings_num_cols(int i) const override {
- TC_DCHECK(InRange(i, embeddings_size()));
- return trimmed_proto_.embeddings(i).cols();
- }
-
- const void *embeddings_weights(int i) const override {
- TC_DCHECK(InRange(i, embeddings_size()));
- const int blob_index = trimmed_proto_.embeddings(i).is_quantized()
- ? (embeddings_blob_offset_ + 2 * i)
- : (embeddings_blob_offset_ + i);
- StringPiece data_blob_view = memory_reader_.data_blob_view(blob_index);
- return data_blob_view.data();
- }
-
- QuantizationType embeddings_quant_type(int i) const override {
- TC_DCHECK(InRange(i, embeddings_size()));
- if (trimmed_proto_.embeddings(i).is_quantized()) {
- return QuantizationType::UINT8;
- } else {
- return QuantizationType::NONE;
- }
- }
-
- const float16 *embeddings_quant_scales(int i) const override {
- TC_DCHECK(InRange(i, embeddings_size()));
- if (trimmed_proto_.embeddings(i).is_quantized()) {
- // Each embedding matrix has two atttached data blobs (hence the "2 * i"):
- // one blob with the quantized values and (immediately after it, hence the
- // "+ 1") one blob with the scales.
- int blob_index = embeddings_blob_offset_ + 2 * i + 1;
- StringPiece data_blob_view = memory_reader_.data_blob_view(blob_index);
- return reinterpret_cast<const float16 *>(data_blob_view.data());
- } else {
- return nullptr;
- }
- }
-
- int hidden_size() const override { return trimmed_proto_.hidden_size(); }
-
- int hidden_num_rows(int i) const override {
- TC_DCHECK(InRange(i, hidden_size()));
- return trimmed_proto_.hidden(i).rows();
- }
-
- int hidden_num_cols(int i) const override {
- TC_DCHECK(InRange(i, hidden_size()));
- return trimmed_proto_.hidden(i).cols();
- }
-
- const void *hidden_weights(int i) const override {
- TC_DCHECK(InRange(i, hidden_size()));
- StringPiece data_blob_view =
- memory_reader_.data_blob_view(hidden_blob_offset_ + i);
- return data_blob_view.data();
- }
-
- int hidden_bias_size() const override {
- return trimmed_proto_.hidden_bias_size();
- }
-
- int hidden_bias_num_rows(int i) const override {
- TC_DCHECK(InRange(i, hidden_bias_size()));
- return trimmed_proto_.hidden_bias(i).rows();
- }
-
- int hidden_bias_num_cols(int i) const override {
- TC_DCHECK(InRange(i, hidden_bias_size()));
- return trimmed_proto_.hidden_bias(i).cols();
- }
-
- const void *hidden_bias_weights(int i) const override {
- TC_DCHECK(InRange(i, hidden_bias_size()));
- StringPiece data_blob_view =
- memory_reader_.data_blob_view(hidden_bias_blob_offset_ + i);
- return data_blob_view.data();
- }
-
- int softmax_size() const override {
- return trimmed_proto_.has_softmax() ? 1 : 0;
- }
-
- int softmax_num_rows(int i) const override {
- TC_DCHECK(InRange(i, softmax_size()));
- return trimmed_proto_.softmax().rows();
- }
-
- int softmax_num_cols(int i) const override {
- TC_DCHECK(InRange(i, softmax_size()));
- return trimmed_proto_.softmax().cols();
- }
-
- const void *softmax_weights(int i) const override {
- TC_DCHECK(InRange(i, softmax_size()));
- StringPiece data_blob_view =
- memory_reader_.data_blob_view(softmax_blob_offset_ + i);
- return data_blob_view.data();
- }
-
- int softmax_bias_size() const override {
- return trimmed_proto_.has_softmax_bias() ? 1 : 0;
- }
-
- int softmax_bias_num_rows(int i) const override {
- TC_DCHECK(InRange(i, softmax_bias_size()));
- return trimmed_proto_.softmax_bias().rows();
- }
-
- int softmax_bias_num_cols(int i) const override {
- TC_DCHECK(InRange(i, softmax_bias_size()));
- return trimmed_proto_.softmax_bias().cols();
- }
-
- const void *softmax_bias_weights(int i) const override {
- TC_DCHECK(InRange(i, softmax_bias_size()));
- StringPiece data_blob_view =
- memory_reader_.data_blob_view(softmax_bias_blob_offset_ + i);
- return data_blob_view.data();
- }
-
- int embedding_num_features_size() const override {
- return trimmed_proto_.embedding_num_features_size();
- }
-
- int embedding_num_features(int i) const override {
- TC_DCHECK(InRange(i, embedding_num_features_size()));
- return trimmed_proto_.embedding_num_features(i);
- }
-
- private:
- MemoryImageReader<EmbeddingNetworkProto> memory_reader_;
-
- const EmbeddingNetworkProto &trimmed_proto_;
-
- // 0-based offsets in the list of data blobs for the different MatrixParams
- // fields. E.g., the 1st hidden MatrixParams has its weights stored in the
- // data blob number hidden_blob_offset_, the 2nd one in hidden_blob_offset_ +
- // 1, and so on.
- int embeddings_blob_offset_;
- int hidden_blob_offset_;
- int hidden_bias_blob_offset_;
- int softmax_blob_offset_;
- int softmax_bias_blob_offset_;
-};
-
-} // namespace nlp_core
-} // namespace libtextclassifier
-
-#endif // LIBTEXTCLASSIFIER_COMMON_MEMORY_IMAGE_EMBEDDING_NETWORK_PARAMS_FROM_IMAGE_H_
diff --git a/common/memory_image/in-memory-model-data.cc b/common/memory_image/in-memory-model-data.cc
deleted file mode 100644
index acf3d86..0000000
--- a/common/memory_image/in-memory-model-data.cc
+++ /dev/null
@@ -1,45 +0,0 @@
-/*
- * Copyright (C) 2017 The Android Open Source Project
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#include "common/memory_image/in-memory-model-data.h"
-
-#include "common/file-utils.h"
-#include "util/base/logging.h"
-#include "util/strings/stringpiece.h"
-
-namespace libtextclassifier {
-namespace nlp_core {
-
-const char InMemoryModelData::kTaskSpecDataStoreEntryName[] = "TASK-SPEC-#@";
-const char InMemoryModelData::kFilePatternPrefix[] = "in-mem-model::";
-
-bool InMemoryModelData::GetTaskSpec(TaskSpec *task_spec) const {
- StringPiece blob = data_store_.GetData(kTaskSpecDataStoreEntryName);
- if (blob.data() == nullptr) {
- TC_LOG(ERROR) << "Can't find data blob for TaskSpec, i.e., entry "
- << kTaskSpecDataStoreEntryName;
- return false;
- }
- bool parse_status = file_utils::ParseProtoFromMemory(blob, task_spec);
- if (!parse_status) {
- TC_LOG(ERROR) << "Error parsing TaskSpec";
- return false;
- }
- return true;
-}
-
-} // namespace nlp_core
-} // namespace libtextclassifier
diff --git a/common/memory_image/in-memory-model-data.h b/common/memory_image/in-memory-model-data.h
deleted file mode 100644
index 91e4436..0000000
--- a/common/memory_image/in-memory-model-data.h
+++ /dev/null
@@ -1,76 +0,0 @@
-/*
- * Copyright (C) 2017 The Android Open Source Project
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#ifndef LIBTEXTCLASSIFIER_COMMON_MEMORY_IMAGE_IN_MEMORY_MODEL_DATA_H_
-#define LIBTEXTCLASSIFIER_COMMON_MEMORY_IMAGE_IN_MEMORY_MODEL_DATA_H_
-
-#include "common/memory_image/data-store.h"
-#include "common/task-spec.pb.h"
-#include "util/strings/stringpiece.h"
-
-namespace libtextclassifier {
-namespace nlp_core {
-
-// In-memory representation of data for a Saft model. Provides access to a
-// TaskSpec object (produced by the "spec" stage of the Saft training model) and
-// to the bytes of the TaskInputs mentioned in that spec (all these bytes are in
-// memory, no file I/O required).
-//
-// Technically, an InMemoryModelData is a DataStore that maps the special string
-// kTaskSpecDataStoreEntryName to the binary serialization of a TaskSpec. For
-// each TaskInput (of the TaskSpec) with a file_pattern that starts with
-// kFilePatternPrefix (see below), the same DataStore maps file_pattern to some
-// content bytes. This way, it is possible to have all TaskInputs in memory,
-// while still allowing classic, on-disk TaskInputs.
-class InMemoryModelData {
- public:
- // Name for the DataStore entry that stores the serialized TaskSpec for the
- // entire model.
- static const char kTaskSpecDataStoreEntryName[];
-
- // Returns prefix for TaskInput::Part::file_pattern, to distinguish those
- // "files" from other files.
- static const char kFilePatternPrefix[];
-
- // Constructs an InMemoryModelData based on a chunk of bytes. Those bytes
- // should have been produced by a DataStoreBuilder.
- explicit InMemoryModelData(StringPiece bytes) : data_store_(bytes) {}
-
- // Fills *task_spec with a TaskSpec similar to the one used by
- // DataStoreBuilder (when building the bytes used to construct this
- // InMemoryModelData) except that each file name
- // (TaskInput::Part::file_pattern) is replaced with a name that can be used to
- // retrieve the corresponding file content bytes via GetBytesForInputFile().
- //
- // Returns true on success, false otherwise.
- bool GetTaskSpec(TaskSpec *task_spec) const;
-
- // Gets content bytes for a file. The file_name argument should be the
- // file_pattern for a TaskInput from the TaskSpec (see GetTaskSpec()).
- // Returns a StringPiece indicating a memory area with the content bytes. On
- // error, returns StringPiece(nullptr, 0).
- StringPiece GetBytesForInputFile(const std::string &file_name) const {
- return data_store_.GetData(file_name);
- }
-
- private:
- const memory_image::DataStore data_store_;
-};
-
-} // namespace nlp_core
-} // namespace libtextclassifier
-
-#endif // LIBTEXTCLASSIFIER_COMMON_MEMORY_IMAGE_IN_MEMORY_MODEL_DATA_H_
diff --git a/common/memory_image/low-level-memory-reader.h b/common/memory_image/low-level-memory-reader.h
deleted file mode 100644
index c87c772..0000000
--- a/common/memory_image/low-level-memory-reader.h
+++ /dev/null
@@ -1,142 +0,0 @@
-/*
- * Copyright (C) 2017 The Android Open Source Project
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#ifndef LIBTEXTCLASSIFIER_COMMON_MEMORY_IMAGE_LOW_LEVEL_MEMORY_READER_H_
-#define LIBTEXTCLASSIFIER_COMMON_MEMORY_IMAGE_LOW_LEVEL_MEMORY_READER_H_
-
-#include <string.h>
-
-#include <string>
-
-#include "util/base/endian.h"
-#include "util/base/integral_types.h"
-#include "util/base/logging.h"
-#include "util/strings/stringpiece.h"
-
-namespace libtextclassifier {
-namespace nlp_core {
-
-class LowLevelMemReader {
- public:
- // Constructs a MemReader instance that reads at most num_available_bytes
- // starting from address start.
- LowLevelMemReader(const void *start, uint64 num_available_bytes)
- : current_(reinterpret_cast<const char *>(start)),
- // 0 bytes available if start == nullptr
- num_available_bytes_(start ? num_available_bytes : 0),
- num_loaded_bytes_(0) {
- }
-
- // Copies length bytes of data to address target. Advances current position
- // and returns true on success and false otherwise.
- bool Read(void *target, uint64 length) {
- if (length > num_available_bytes_) {
- TC_LOG(WARNING) << "Not enough bytes: available " << num_available_bytes_
- << " < required " << length;
- return false;
- }
- memcpy(target, current_, length);
- Advance(length);
- return true;
- }
-
- // Reads the string encoded at the current position. The bytes starting at
- // current position should contain (1) little-endian uint32 size (in bytes) of
- // the actual string and next (2) the actual bytes of the string. Advances
- // the current position and returns true if successful, false otherwise.
- //
- // On success, sets *view to be a view of the relevant bytes: view.data()
- // points to the beginning of the string bytes, and view.size() is the number
- // of such bytes.
- bool ReadString(StringPiece *view) {
- uint32 size;
- if (!Read(&size, sizeof(size))) {
- TC_LOG(ERROR) << "Unable to read std::string size";
- return false;
- }
- size = LittleEndian::ToHost32(size);
- if (size > num_available_bytes_) {
- TC_LOG(WARNING) << "Not enough bytes: " << num_available_bytes_
- << " available < " << size << " required ";
- return false;
- }
- *view = StringPiece(current_, size);
- Advance(size);
- return true;
- }
-
- // Like ReadString(StringPiece *) but reads directly into a C++ string,
- // instead of a StringPiece (StringPiece-like object).
- bool ReadString(std::string *target) {
- StringPiece view;
- if (!ReadString(&view)) {
- return false;
- }
- *target = view.ToString();
- return true;
- }
-
- // Returns current position.
- const char *GetCurrent() const { return current_; }
-
- // Returns remaining number of available bytes.
- uint64 GetNumAvailableBytes() const { return num_available_bytes_; }
-
- // Returns number of bytes read ("loaded") so far.
- uint64 GetNumLoadedBytes() const { return num_loaded_bytes_; }
-
- // Advance the current read position by indicated number of bytes. Returns
- // true on success, false otherwise (e.g., if there are not enough available
- // bytes to advance num_bytes).
- bool Advance(uint64 num_bytes) {
- if (num_bytes > num_available_bytes_) {
- return false;
- }
-
- // Next line never results in an underflow of the unsigned
- // num_available_bytes_, due to the previous if.
- num_available_bytes_ -= num_bytes;
- current_ += num_bytes;
- num_loaded_bytes_ += num_bytes;
- return true;
- }
-
- // Advance current position to nearest multiple of alignment. Returns false
- // if not enough bytes available to do that, true (success) otherwise.
- bool SkipToAlign(int alignment) {
- int num_extra_bytes = num_loaded_bytes_ % alignment;
- if (num_extra_bytes == 0) {
- return true;
- }
- return Advance(alignment - num_extra_bytes);
- }
-
- private:
- // Current position in the in-memory data. Next call to Read() will read from
- // this address.
- const char *current_;
-
- // Number of available bytes we can still read.
- uint64 num_available_bytes_;
-
- // Number of bytes read ("loaded") so far.
- uint64 num_loaded_bytes_;
-};
-
-} // namespace nlp_core
-} // namespace libtextclassifier
-
-#endif // LIBTEXTCLASSIFIER_COMMON_MEMORY_IMAGE_LOW_LEVEL_MEMORY_READER_H_
diff --git a/common/memory_image/memory-image-common.cc b/common/memory_image/memory-image-common.cc
deleted file mode 100644
index 6debf1d..0000000
--- a/common/memory_image/memory-image-common.cc
+++ /dev/null
@@ -1,31 +0,0 @@
-/*
- * Copyright (C) 2017 The Android Open Source Project
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#include "common/memory_image/memory-image-common.h"
-
-namespace libtextclassifier {
-namespace nlp_core {
-
-// IMPORTANT: this signature should never change. If you change the protocol,
-// update kCurrentVersion, *not* this signature.
-const char MemoryImageConstants::kSignature[] = "Memory image $5%1#o3-1x32";
-
-const int MemoryImageConstants::kCurrentVersion = 1;
-
-const int MemoryImageConstants::kDefaultAlignment = 16;
-
-} // namespace nlp_core
-} // namespace libtextclassifier
diff --git a/common/memory_image/memory-image-common.h b/common/memory_image/memory-image-common.h
deleted file mode 100644
index 3a46f49..0000000
--- a/common/memory_image/memory-image-common.h
+++ /dev/null
@@ -1,41 +0,0 @@
-/*
- * Copyright (C) 2017 The Android Open Source Project
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-// Common utils for memory images.
-
-#ifndef LIBTEXTCLASSIFIER_COMMON_MEMORY_IMAGE_MEMORY_IMAGE_COMMON_H_
-#define LIBTEXTCLASSIFIER_COMMON_MEMORY_IMAGE_MEMORY_IMAGE_COMMON_H_
-
-#include <stddef.h>
-
-#include <string>
-
-#include "util/strings/stringpiece.h"
-
-namespace libtextclassifier {
-namespace nlp_core {
-
-class MemoryImageConstants {
- public:
- static const char kSignature[];
- static const int kCurrentVersion;
- static const int kDefaultAlignment;
-};
-
-} // namespace nlp_core
-} // namespace libtextclassifier
-
-#endif // LIBTEXTCLASSIFIER_COMMON_MEMORY_IMAGE_MEMORY_IMAGE_COMMON_H_
diff --git a/common/memory_image/memory-image-reader.cc b/common/memory_image/memory-image-reader.cc
deleted file mode 100644
index 7e717d5..0000000
--- a/common/memory_image/memory-image-reader.cc
+++ /dev/null
@@ -1,125 +0,0 @@
-/*
- * Copyright (C) 2017 The Android Open Source Project
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#include "common/memory_image/memory-image-reader.h"
-
-#include <string>
-
-#include "common/memory_image/low-level-memory-reader.h"
-#include "common/memory_image/memory-image-common.h"
-#include "common/memory_image/memory-image.pb.h"
-#include "util/base/endian.h"
-#include "util/base/logging.h"
-
-namespace libtextclassifier {
-namespace nlp_core {
-
-namespace {
-
-// Checks that the memory area read by mem_reader starts with the expected
-// signature. Advances mem_reader past the signature and returns success
-// status.
-bool ReadAndCheckSignature(LowLevelMemReader *mem_reader) {
- const std::string expected_signature = MemoryImageConstants::kSignature;
- const int signature_size = expected_signature.size();
- if (mem_reader->GetNumAvailableBytes() < signature_size) {
- TC_LOG(ERROR) << "Not enough bytes to check signature";
- return false;
- }
- const std::string actual_signature(mem_reader->GetCurrent(), signature_size);
- if (!mem_reader->Advance(signature_size)) {
- TC_LOG(ERROR) << "Failed to advance past signature";
- return false;
- }
- if (actual_signature != expected_signature) {
- TC_LOG(ERROR) << "Different signature: actual \"" << actual_signature
- << "\" != expected \"" << expected_signature << "\"";
- return false;
- }
- return true;
-}
-
-// Parses MemoryImageHeader from mem_reader. Advances mem_reader past it.
-// Returns success status.
-bool ParseMemoryImageHeader(
- LowLevelMemReader *mem_reader, MemoryImageHeader *header) {
- std::string header_proto_str;
- if (!mem_reader->ReadString(&header_proto_str)) {
- TC_LOG(ERROR) << "Unable to read header_proto_str";
- return false;
- }
- if (!header->ParseFromString(header_proto_str)) {
- TC_LOG(ERROR) << "Unable to parse MemoryImageHeader";
- return false;
- }
- return true;
-}
-
-} // namespace
-
-bool GeneralMemoryImageReader::ReadMemoryImage() {
- LowLevelMemReader mem_reader(start_, num_bytes_);
-
- // Read and check signature.
- if (!ReadAndCheckSignature(&mem_reader)) {
- return false;
- }
-
- // Parse MemoryImageHeader header_.
- if (!ParseMemoryImageHeader(&mem_reader, &header_)) {
- return false;
- }
-
- // Check endianness.
- if (header_.is_little_endian() != LittleEndian::IsLittleEndian()) {
- // TODO(salcianu): implement conversion: it will take time, but it's better
- // than crashing. Not very urgent: [almost] all current Android phones are
- // little-endian.
- TC_LOG(ERROR) << "Memory image is "
- << (header_.is_little_endian() ? "little" : "big")
- << " endian. "
- << "Local system is different and we don't currently support "
- << "conversion between the two.";
- return false;
- }
-
- // Read binary serialization of trimmed original proto.
- if (!mem_reader.ReadString(&trimmed_proto_serialization_)) {
- TC_LOG(ERROR) << "Unable to read trimmed proto binary serialization";
- return false;
- }
-
- // Fill vector of pointers to beginning of each data blob.
- for (int i = 0; i < header_.blob_info_size(); ++i) {
- const MemoryImageDataBlobInfo &blob_info = header_.blob_info(i);
- if (!mem_reader.SkipToAlign(header_.alignment())) {
- TC_LOG(ERROR) << "Unable to align for blob #i" << i;
- return false;
- }
- data_blob_views_.emplace_back(
- mem_reader.GetCurrent(),
- blob_info.num_bytes());
- if (!mem_reader.Advance(blob_info.num_bytes())) {
- TC_LOG(ERROR) << "Not enough bytes for blob #i" << i;
- return false;
- }
- }
-
- return true;
-}
-
-} // namespace nlp_core
-} // namespace libtextclassifier
diff --git a/common/memory_image/memory-image-reader.h b/common/memory_image/memory-image-reader.h
deleted file mode 100644
index c5954fd..0000000
--- a/common/memory_image/memory-image-reader.h
+++ /dev/null
@@ -1,154 +0,0 @@
-/*
- * Copyright (C) 2017 The Android Open Source Project
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-// MemoryImageReader, class for reading a memory image.
-
-#ifndef LIBTEXTCLASSIFIER_COMMON_MEMORY_IMAGE_MEMORY_IMAGE_READER_H_
-#define LIBTEXTCLASSIFIER_COMMON_MEMORY_IMAGE_MEMORY_IMAGE_READER_H_
-
-#include <string>
-#include <vector>
-
-#include "common/memory_image/memory-image.pb.h"
-#include "util/base/integral_types.h"
-#include "util/base/logging.h"
-#include "util/base/macros.h"
-#include "util/strings/stringpiece.h"
-
-namespace libtextclassifier {
-namespace nlp_core {
-
-// General, non-templatized class, to reduce code duplication.
-//
-// Given a memory area (pointer to start + size in bytes) parses a memory image
-// from there into (1) MemoryImageHeader proto (it includes the serialized form
-// of the trimmed down original proto) and (2) a list of void* pointers to the
-// beginning of all data blobs.
-//
-// In case of parsing errors, we prefer to log the error and set the
-// success_status() to false, instead of CHECK-failing . This way, the client
-// has the option of performing error recovery or crashing. Some mobile apps
-// don't like crashing (a restart is very slow) so, if possible, we try to avoid
-// that.
-class GeneralMemoryImageReader {
- public:
- // Constructs this object. See class-level comments. Note: the memory area
- // pointed to by start should not be deallocated while this object is used:
- // this object does not copy it; instead, it keeps pointers inside that memory
- // area.
- GeneralMemoryImageReader(const void *start, uint64 num_bytes)
- : start_(start), num_bytes_(num_bytes) {
- success_ = ReadMemoryImage();
- }
-
- virtual ~GeneralMemoryImageReader() {}
-
- // Returns true if reading the memory image has been successful. If this
- // returns false, then none of the other accessors should be used.
- bool success_status() const { return success_; }
-
- // Returns number of data blobs from the memory image.
- int num_data_blobs() const {
- return data_blob_views_.size();
- }
-
- // Returns pointer to the beginning of the data blob #i.
- StringPiece data_blob_view(int i) const {
- if ((i < 0) || (i >= num_data_blobs())) {
- TC_LOG(ERROR) << "Blob index " << i << " outside range [0, "
- << num_data_blobs() << "); will return empty data chunk";
- return StringPiece();
- }
- return data_blob_views_[i];
- }
-
- // Returns std::string with binary serialization of the original proto, but
- // trimmed of the large fields (those were placed in the data blobs).
- std::string trimmed_proto_str() const {
- return trimmed_proto_serialization_.ToString();
- }
-
- // Same as above but returns the trimmed proto as a string piece pointing to
- // the image.
- StringPiece trimmed_proto_view() const {
- return trimmed_proto_serialization_;
- }
-
- const MemoryImageHeader &header() { return header_; }
-
- protected:
- void set_as_failed() {
- success_ = false;
- }
-
- private:
- bool ReadMemoryImage();
-
- // Pointer to beginning of memory image. Not owned.
- const void *const start_;
-
- // Number of bytes in the memory image. This class will not read more bytes.
- const uint64 num_bytes_;
-
- // MemoryImageHeader parsed from the memory image.
- MemoryImageHeader header_;
-
- // Binary serialization of the trimmed version of the original proto.
- // Represented as a StringPiece backed up by the underlying memory image
- // bytes.
- StringPiece trimmed_proto_serialization_;
-
- // List of StringPiece objects for all data blobs from the memory image (in
- // order).
- std::vector<StringPiece> data_blob_views_;
-
- // Memory reading success status.
- bool success_;
-
- TC_DISALLOW_COPY_AND_ASSIGN(GeneralMemoryImageReader);
-};
-
-// Like GeneralMemoryImageReader, but has knowledge about the type of the
-// original proto. As such, it can parse it (well, the trimmed version) and
-// offer access to it.
-//
-// Template parameter T should be the type of the original proto.
-template<class T>
-class MemoryImageReader : public GeneralMemoryImageReader {
- public:
- MemoryImageReader(const void *start, uint64 num_bytes)
- : GeneralMemoryImageReader(start, num_bytes) {
- if (!trimmed_proto_.ParseFromString(trimmed_proto_str())) {
- TC_LOG(INFO) << "Unable to parse the trimmed proto";
- set_as_failed();
- }
- }
-
- // Returns const reference to the trimmed version of the original proto.
- // Useful for retrieving the many small fields that are not converted into
- // data blobs.
- const T &trimmed_proto() const { return trimmed_proto_; }
-
- private:
- T trimmed_proto_;
-
- TC_DISALLOW_COPY_AND_ASSIGN(MemoryImageReader);
-};
-
-} // namespace nlp_core
-} // namespace libtextclassifier
-
-#endif // LIBTEXTCLASSIFIER_COMMON_MEMORY_IMAGE_MEMORY_IMAGE_READER_H_
diff --git a/common/memory_image/memory-image.proto b/common/memory_image/memory-image.proto
deleted file mode 100644
index f6b624c..0000000
--- a/common/memory_image/memory-image.proto
+++ /dev/null
@@ -1,63 +0,0 @@
-// Copyright (C) 2017 The Android Open Source Project
-//
-// Licensed under the Apache License, Version 2.0 (the "License");
-// you may not use this file except in compliance with the License.
-// You may obtain a copy of the License at
-//
-// http://www.apache.org/licenses/LICENSE-2.0
-//
-// Unless required by applicable law or agreed to in writing, software
-// distributed under the License is distributed on an "AS IS" BASIS,
-// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-// See the License for the specific language governing permissions and
-// limitations under the License.
-
-// Protos for "memory images".
-
-syntax = "proto2";
-option optimize_for = LITE_RUNTIME;
-
-package libtextclassifier.nlp_core;
-
-message MemoryImageDataBlobInfo {
- // Size (in bytes) of this data blob.
- optional uint64 num_bytes = 1;
-
- // Indicates whether this data blob corresponds to an array.
- optional bool is_array = 2 [default = true];
-
- // Size (in bytes) of each array element. Useful for little <-> big endian
- // conversions. -1 means unknown: no endianness conversion in that case.
- optional int32 element_size = 3 [default = -1];
-}
-
-message MemoryImageHeader {
- // Version of the algorithm used to produce the memory image. We should
- // increase the value used here every time we perform an incompatible change.
- // Algorithm version v should handle only memory images of the same version,
- // and crash otherwise.
- optional int32 version = 1 [default = -1];
-
- // True if the info stored in the data blobs uses the little endian
- // convention. Almost all machines today are little-endian but we want to be
- // able to crash with an informative message or perform a (costly) conversion
- // in the rare cases when that's not true.
- optional bool is_little_endian = 2 [default = true];
-
- // Alignment (in bytes) for all data blobs. E.g., if this field is 16, then
- // each data blob starts at an offset that's a multiple of 16, where the
- // offset is measured from the beginning of the memory image. On the client
- // side, allocating the entire memory image at an aligned address (by same
- // alignment) makes sure all data blobs are properly aligned.
- //
- // NOTE: I (salcianu) explored the idea of a different alignment for each data
- // blob: e.g., float[] should be fine with 4-byte alignment (alignment = 4)
- // but char[] are fine with no alignment (alignment = 1). As we expect only a
- // few (but large) data blobs, the space benefit is not worth the extra code
- // complexity.
- optional int32 alignment = 3 [default = 8];
-
- // One MemoryImageDataBlobInfo for each data blob, in order. There is one
- // data blob for each large field we handle specially.
- repeated MemoryImageDataBlobInfo blob_info = 4;
-}
diff --git a/common/mock_functions.h b/common/mock_functions.h
deleted file mode 100644
index b5bcb07..0000000
--- a/common/mock_functions.h
+++ /dev/null
@@ -1,75 +0,0 @@
-/*
- * Copyright (C) 2017 The Android Open Source Project
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#ifndef LIBTEXTCLASSIFIER_COMMON_MOCK_FUNCTIONS_H_
-#define LIBTEXTCLASSIFIER_COMMON_MOCK_FUNCTIONS_H_
-
-#include <math.h>
-
-#include "common/registry.h"
-
-namespace libtextclassifier {
-namespace nlp_core {
-namespace functions {
-
-// Abstract double -> double function.
-class Function : public RegisterableClass<Function> {
- public:
- virtual ~Function() {}
- virtual double Evaluate(double x) = 0;
-};
-
-class Cos : public Function {
- public:
- double Evaluate(double x) override { return cos(x); }
- TC_DEFINE_REGISTRATION_METHOD("cos", Cos);
-};
-
-class Exp : public Function {
- public:
- double Evaluate(double x) override { return exp(x); }
- TC_DEFINE_REGISTRATION_METHOD("exp", Exp);
-};
-
-// Abstract int -> int function.
-class IntFunction : public RegisterableClass<IntFunction> {
- public:
- virtual ~IntFunction() {}
- virtual int Evaluate(int k) = 0;
-};
-
-class Inc : public IntFunction {
- public:
- int Evaluate(int k) override { return k + 1; }
- TC_DEFINE_REGISTRATION_METHOD("inc", Inc);
-};
-
-class Dec : public IntFunction {
- public:
- int Evaluate(int k) override { return k + 1; }
- TC_DEFINE_REGISTRATION_METHOD("dec", Dec);
-};
-
-} // namespace functions
-
-// Should be inside namespace libtextclassifier::nlp_core.
-TC_DECLARE_CLASS_REGISTRY_NAME(functions::Function);
-TC_DECLARE_CLASS_REGISTRY_NAME(functions::IntFunction);
-
-} // namespace nlp_core
-} // namespace libtextclassifier
-
-#endif // LIBTEXTCLASSIFIER_COMMON_MOCK_FUNCTIONS_H_
diff --git a/common/registry.h b/common/registry.h
deleted file mode 100644
index d958225..0000000
--- a/common/registry.h
+++ /dev/null
@@ -1,281 +0,0 @@
-/*
- * Copyright (C) 2017 The Android Open Source Project
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-// Mechanism to instantiate classes by name.
-//
-// This mechanism is useful if the concrete classes to be instantiated are not
-// statically known (e.g., if their names are read from a dynamically-provided
-// config).
-//
-// In that case, the first step is to define the API implemented by the
-// instantiated classes. E.g.,
-//
-// // In a header file function.h:
-//
-// // Abstract function that takes a double and returns a double.
-// class Function : public RegisterableClass<Function> {
-// public:
-// virtual ~Function() {}
-// virtual double Evaluate(double x) = 0;
-// };
-//
-// // Should be inside namespace libtextclassifier::nlp_core.
-// TC_DECLARE_CLASS_REGISTRY_NAME(Function);
-//
-// Notice the inheritance from RegisterableClass<Function>. RegisterableClass
-// is defined by this file (registry.h). Under the hood, this inheritanace
-// defines a "registry" that maps names (zero-terminated arrays of chars) to
-// factory methods that create Functions. You should give a human-readable name
-// to this registry. To do that, use the following macro in a .cc file (it has
-// to be a .cc file, as it defines some static data):
-//
-// // Inside function.cc
-// // Should be inside namespace libtextclassifier::nlp_core.
-// TC_DEFINE_CLASS_REGISTRY_NAME("function", Function);
-//
-// Now, let's define a few concrete Functions: e.g.,
-//
-// class Cos : public Function {
-// public:
-// double Evaluate(double x) override { return cos(x); }
-// TC_DEFINE_REGISTRATION_METHOD("cos", Cos);
-// };
-//
-// class Exp : public Function {
-// public:
-// double Evaluate(double x) override { return exp(x); }
-// TC_DEFINE_REGISTRATION_METHOD("sin", Sin);
-// };
-//
-// Each concrete Function implementation should have (in the public section) the
-// macro
-//
-// TC_DEFINE_REGISTRATION_METHOD("name", implementation_class);
-//
-// This defines a RegisterClass static method that, when invoked, associates
-// "name" with a factory method that creates instances of implementation_class.
-//
-// Before instantiating Functions by name, we need to tell our system which
-// Functions we may be interested in. This is done by calling the
-// Foo::RegisterClass() for each relevant Foo implementation of Function. It is
-// ok to call Foo::RegisterClass() multiple times (even in parallel): only the
-// first call will perform something, the others will return immediately.
-//
-// Cos::RegisterClass();
-// Exp::RegisterClass();
-//
-// Now, let's instantiate a Function based on its name. This get a lot more
-// interesting if the Function name is not statically known (i.e.,
-// read from an input proto:
-//
-// std::unique_ptr<Function> f(Function::Create("cos"));
-// double result = f->Evaluate(arg);
-//
-// NOTE: the same binary can use this mechanism for different APIs. E.g., one
-// can also have (in the binary with Function, Sin, Cos, etc):
-//
-// class IntFunction : public RegisterableClass<IntFunction> {
-// public:
-// virtual ~IntFunction() {}
-// virtual int Evaluate(int k) = 0;
-// };
-//
-// TC_DECLARE_CLASS_REGISTRY_NAME(IntFunction);
-//
-// TC_DEFINE_CLASS_REGISTRY_NAME("int function", IntFunction);
-//
-// class Inc : public IntFunction {
-// public:
-// int Evaluate(int k) override { return k + 1; }
-// TC_DEFINE_REGISTRATION_METHOD("inc", Inc);
-// };
-//
-// RegisterableClass<Function> and RegisterableClass<IntFunction> define their
-// own registries: each maps string names to implementation of the corresponding
-// API.
-
-#ifndef LIBTEXTCLASSIFIER_COMMON_REGISTRY_H_
-#define LIBTEXTCLASSIFIER_COMMON_REGISTRY_H_
-
-#include <stdlib.h>
-#include <string.h>
-
-#include <string>
-
-#include "util/base/logging.h"
-
-namespace libtextclassifier {
-namespace nlp_core {
-
-namespace internal {
-// Registry that associates keys (zero-terminated array of chars) with values.
-// Values are pointers to type T (the template parameter). This is used to
-// store the association between component names and factory methods that
-// produce those components; the error messages are focused on that case.
-//
-// Internally, this registry uses a linked list of (key, value) pairs. We do
-// not use an STL map, list, etc because we aim for small code size.
-template <class T>
-class ComponentRegistry {
- public:
- explicit ComponentRegistry(const char *name) : name_(name), head_(nullptr) {}
-
- // Adds a the (key, value) pair to this registry (if the key does not already
- // exists in this registry) and returns true. If the registry already has a
- // mapping for key, returns false and does not modify the registry. NOTE: the
- // error (false) case happens even if the existing value for key is equal with
- // the new one.
- //
- // This method does not take ownership of key, nor of value.
- bool Add(const char *key, T *value) {
- const Cell *old_cell = FindCell(key);
- if (old_cell != nullptr) {
- TC_LOG(ERROR) << "Duplicate component: " << key;
- return false;
- }
- Cell *new_cell = new Cell(key, value, head_);
- head_ = new_cell;
- return true;
- }
-
- // Returns the value attached to a key in this registry. Returns nullptr on
- // error (e.g., unknown key).
- T *Lookup(const char *key) const {
- const Cell *cell = FindCell(key);
- if (cell == nullptr) {
- TC_LOG(ERROR) << "Unknown " << name() << " component: " << key;
- }
- return (cell == nullptr) ? nullptr : cell->value();
- }
-
- T *Lookup(const std::string &key) const { return Lookup(key.c_str()); }
-
- // Returns name of this ComponentRegistry.
- const char *name() const { return name_; }
-
- private:
- // Cell for the singly-linked list underlying this ComponentRegistry. Each
- // cell contains a key, the value for that key, as well as a pointer to the
- // next Cell from the list.
- class Cell {
- public:
- // Constructs a new Cell.
- Cell(const char *key, T *value, Cell *next)
- : key_(key), value_(value), next_(next) {}
-
- const char *key() const { return key_; }
- T *value() const { return value_; }
- Cell *next() const { return next_; }
-
- private:
- const char *const key_;
- T *const value_;
- Cell *const next_;
- };
-
- // Finds Cell for indicated key in the singly-linked list pointed to by head_.
- // Returns pointer to that first Cell with that key, or nullptr if no such
- // Cell (i.e., unknown key).
- //
- // Caller does NOT own the returned pointer.
- const Cell *FindCell(const char *key) const {
- Cell *c = head_;
- while (c != nullptr && strcmp(key, c->key()) != 0) {
- c = c->next();
- }
- return c;
- }
-
- // Human-readable description for this ComponentRegistry. For debug purposes.
- const char *const name_;
-
- // Pointer to the first Cell from the underlying list of (key, value) pairs.
- Cell *head_;
-};
-} // namespace internal
-
-// Base class for registerable classes.
-template <class T>
-class RegisterableClass {
- public:
- // Factory function type.
- typedef T *(Factory)();
-
- // Registry type.
- typedef internal::ComponentRegistry<Factory> Registry;
-
- // Creates a new instance of T. Returns pointer to new instance or nullptr in
- // case of errors (e.g., unknown component).
- //
- // Passes ownership of the returned pointer to the caller.
- static T *Create(const std::string &name) { // NOLINT
- auto *factory = registry()->Lookup(name);
- if (factory == nullptr) {
- TC_LOG(ERROR) << "Unknown RegisterableClass " << name;
- return nullptr;
- }
- return factory();
- }
-
- // Returns registry for class.
- static Registry *registry() {
- static Registry *registry_for_type_t = new Registry(kRegistryName);
- return registry_for_type_t;
- }
-
- protected:
- // Factory method for subclass ComponentClass. Used internally by the static
- // method RegisterClass() defined by TC_DEFINE_REGISTRATION_METHOD.
- template <class ComponentClass>
- static T *_internal_component_factory() {
- return new ComponentClass();
- }
-
- private:
- // Human-readable name for the registry for this class.
- static const char kRegistryName[];
-};
-
-// Defines the static method component_class::RegisterClass() that should be
-// called before trying to instantiate component_class by name. Should be used
-// inside the public section of the declaration of component_class. See
-// comments at the top-level of this file.
-#define TC_DEFINE_REGISTRATION_METHOD(component_name, component_class) \
- static void RegisterClass() { \
- static bool once = registry()->Add( \
- component_name, &_internal_component_factory<component_class>); \
- if (!once) { \
- TC_LOG(ERROR) << "Problem registering " << component_name; \
- } \
- TC_DCHECK(once); \
- }
-
-// Defines the human-readable name of the registry associated with base_class.
-#define TC_DECLARE_CLASS_REGISTRY_NAME(base_class) \
- template <> \
- const char ::libtextclassifier::nlp_core::RegisterableClass< \
- base_class>::kRegistryName[]
-
-// Defines the human-readable name of the registry associated with base_class.
-#define TC_DEFINE_CLASS_REGISTRY_NAME(registry_name, base_class) \
- template <> \
- const char ::libtextclassifier::nlp_core::RegisterableClass< \
- base_class>::kRegistryName[] = registry_name
-
-} // namespace nlp_core
-} // namespace libtextclassifier
-
-#endif // LIBTEXTCLASSIFIER_COMMON_REGISTRY_H_
diff --git a/common/registry_test.cc b/common/registry_test.cc
deleted file mode 100644
index d5d7006..0000000
--- a/common/registry_test.cc
+++ /dev/null
@@ -1,68 +0,0 @@
-/*
- * Copyright (C) 2017 The Android Open Source Project
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#include <memory>
-
-#include "common/mock_functions.h"
-#include "gtest/gtest.h"
-
-namespace libtextclassifier {
-namespace nlp_core {
-namespace functions {
-
-TEST(RegistryTest, InstantiateFunctionsByName) {
- // First, we need to register the functions we are interested in:
- Exp::RegisterClass();
- Inc::RegisterClass();
- Cos::RegisterClass();
-
- // RegisterClass methods can be called in any order, even multiple times :)
- Cos::RegisterClass();
- Inc::RegisterClass();
- Inc::RegisterClass();
- Cos::RegisterClass();
- Inc::RegisterClass();
-
- // NOTE: we intentionally do not register Dec. Attempts to create an instance
- // of that function by name should fail.
-
- // Instantiate a few functions and check that the created functions produce
- // the expected results for a few sample values.
- std::unique_ptr<Function> f1(Function::Create("cos"));
- ASSERT_NE(f1, nullptr);
- std::unique_ptr<Function> f2(Function::Create("exp"));
- ASSERT_NE(f2, nullptr);
- EXPECT_NEAR(f1->Evaluate(-3), -0.9899, 0.0001);
- EXPECT_NEAR(f2->Evaluate(2.3), 9.9741, 0.0001);
-
- std::unique_ptr<IntFunction> f3(IntFunction::Create("inc"));
- ASSERT_NE(f3, nullptr);
- EXPECT_EQ(f3->Evaluate(7), 8);
-
- // Instantiating unknown functions should return nullptr, but not crash
- // anything.
- EXPECT_EQ(Function::Create("mambo"), nullptr);
-
- // Functions that are defined in the code, but are not registered are unknown.
- EXPECT_EQ(IntFunction::Create("dec"), nullptr);
-
- // Function and IntFunction use different registries.
- EXPECT_EQ(IntFunction::Create("exp"), nullptr);
-}
-
-} // namespace functions
-} // namespace nlp_core
-} // namespace libtextclassifier
diff --git a/common/simple-adder.h b/common/simple-adder.h
deleted file mode 100644
index c16cc8a..0000000
--- a/common/simple-adder.h
+++ /dev/null
@@ -1,68 +0,0 @@
-/*
- * Copyright (C) 2017 The Android Open Source Project
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#ifndef LIBTEXTCLASSIFIER_COMMON_SIMPLE_ADDER_H_
-#define LIBTEXTCLASSIFIER_COMMON_SIMPLE_ADDER_H_
-
-#include "util/base/integral_types.h"
-#include "util/base/port.h"
-
-namespace libtextclassifier {
-namespace nlp_core {
-
-// Implements add and scaleadd in the most straight-forward way, and it doesn't
-// have any additional requirement on the alignment and array size.
-class SimpleAdder {
- public:
- TC_ATTRIBUTE_ALWAYS_INLINE SimpleAdder(float *dest, int num_floats)
- : dest_(dest), num_floats_(num_floats) {}
-
- TC_ATTRIBUTE_ALWAYS_INLINE void LazyAdd(const float *source) const {
- AddImpl(source, num_floats_, dest_);
- }
-
- TC_ATTRIBUTE_ALWAYS_INLINE void LazyScaleAdd(const float *source,
- const float scale) const {
- ScaleAddImpl(source, num_floats_, scale, dest_);
- }
-
- // Simple fast while loop to implement dest += source.
- TC_ATTRIBUTE_ALWAYS_INLINE static void AddImpl(const float *__restrict source,
- uint32 size,
- float *__restrict dest) {
- for (uint32 i = 0; i < size; ++i) {
- dest[i] += source[i];
- }
- }
-
- // Simple fast while loop to implement dest += scale * source.
- TC_ATTRIBUTE_ALWAYS_INLINE static void ScaleAddImpl(
- const float *__restrict source, uint32 size, const float scale,
- float *__restrict dest) {
- for (uint32 i = 0; i < size; ++i) {
- dest[i] += source[i] * scale;
- }
- }
-
- private:
- float *dest_;
- int num_floats_;
-};
-
-} // namespace nlp_core
-} // namespace libtextclassifier
-
-#endif // LIBTEXTCLASSIFIER_COMMON_SIMPLE_ADDER_H_
diff --git a/common/task-context.cc b/common/task-context.cc
deleted file mode 100644
index e4c1090..0000000
--- a/common/task-context.cc
+++ /dev/null
@@ -1,206 +0,0 @@
-/*
- * Copyright (C) 2017 The Android Open Source Project
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#include "common/task-context.h"
-
-#include <stdlib.h>
-
-#include <string>
-
-#include "util/base/integral_types.h"
-#include "util/base/logging.h"
-#include "util/strings/numbers.h"
-
-namespace libtextclassifier {
-namespace nlp_core {
-
-namespace {
-int32 ParseInt32WithDefault(const std::string &s, int32 defval) {
- int32 value = defval;
- return ParseInt32(s.c_str(), &value) ? value : defval;
-}
-
-int64 ParseInt64WithDefault(const std::string &s, int64 defval) {
- int64 value = defval;
- return ParseInt64(s.c_str(), &value) ? value : defval;
-}
-
-double ParseDoubleWithDefault(const std::string &s, double defval) {
- double value = defval;
- return ParseDouble(s.c_str(), &value) ? value : defval;
-}
-} // namespace
-
-TaskInput *TaskContext::GetInput(const std::string &name) {
- // Return existing input if it exists.
- for (int i = 0; i < spec_.input_size(); ++i) {
- if (spec_.input(i).name() == name) return spec_.mutable_input(i);
- }
-
- // Create new input.
- TaskInput *input = spec_.add_input();
- input->set_name(name);
- return input;
-}
-
-TaskInput *TaskContext::GetInput(const std::string &name,
- const std::string &file_format,
- const std::string &record_format) {
- TaskInput *input = GetInput(name);
- if (!file_format.empty()) {
- bool found = false;
- for (int i = 0; i < input->file_format_size(); ++i) {
- if (input->file_format(i) == file_format) found = true;
- }
- if (!found) input->add_file_format(file_format);
- }
- if (!record_format.empty()) {
- bool found = false;
- for (int i = 0; i < input->record_format_size(); ++i) {
- if (input->record_format(i) == record_format) found = true;
- }
- if (!found) input->add_record_format(record_format);
- }
- return input;
-}
-
-void TaskContext::SetParameter(const std::string &name,
- const std::string &value) {
- TC_LOG(INFO) << "SetParameter(" << name << ", " << value << ")";
-
- // If the parameter already exists update the value.
- for (int i = 0; i < spec_.parameter_size(); ++i) {
- if (spec_.parameter(i).name() == name) {
- spec_.mutable_parameter(i)->set_value(value);
- return;
- }
- }
-
- // Add new parameter.
- TaskSpec::Parameter *param = spec_.add_parameter();
- param->set_name(name);
- param->set_value(value);
-}
-
-std::string TaskContext::GetParameter(const std::string &name) const {
- // First try to find parameter in task specification.
- for (int i = 0; i < spec_.parameter_size(); ++i) {
- if (spec_.parameter(i).name() == name) return spec_.parameter(i).value();
- }
-
- // Parameter not found, return empty std::string.
- return "";
-}
-
-int TaskContext::GetIntParameter(const std::string &name) const {
- std::string value = GetParameter(name);
- return ParseInt32WithDefault(value, 0);
-}
-
-int64 TaskContext::GetInt64Parameter(const std::string &name) const {
- std::string value = GetParameter(name);
- return ParseInt64WithDefault(value, 0);
-}
-
-bool TaskContext::GetBoolParameter(const std::string &name) const {
- std::string value = GetParameter(name);
- return value == "true";
-}
-
-double TaskContext::GetFloatParameter(const std::string &name) const {
- std::string value = GetParameter(name);
- return ParseDoubleWithDefault(value, 0.0);
-}
-
-std::string TaskContext::Get(const std::string &name,
- const char *defval) const {
- // First try to find parameter in task specification.
- for (int i = 0; i < spec_.parameter_size(); ++i) {
- if (spec_.parameter(i).name() == name) return spec_.parameter(i).value();
- }
-
- // Parameter not found, return default value.
- return defval;
-}
-
-std::string TaskContext::Get(const std::string &name,
- const std::string &defval) const {
- return Get(name, defval.c_str());
-}
-
-int TaskContext::Get(const std::string &name, int defval) const {
- std::string value = Get(name, "");
- return ParseInt32WithDefault(value, defval);
-}
-
-int64 TaskContext::Get(const std::string &name, int64 defval) const {
- std::string value = Get(name, "");
- return ParseInt64WithDefault(value, defval);
-}
-
-double TaskContext::Get(const std::string &name, double defval) const {
- std::string value = Get(name, "");
- return ParseDoubleWithDefault(value, defval);
-}
-
-bool TaskContext::Get(const std::string &name, bool defval) const {
- std::string value = Get(name, "");
- return value.empty() ? defval : value == "true";
-}
-
-std::string TaskContext::InputFile(const TaskInput &input) {
- if (input.part_size() == 0) {
- TC_LOG(ERROR) << "No file for TaskInput " << input.name();
- return "";
- }
- if (input.part_size() > 1) {
- TC_LOG(ERROR) << "Ambiguous: multiple files for TaskInput " << input.name();
- }
- return input.part(0).file_pattern();
-}
-
-bool TaskContext::Supports(const TaskInput &input,
- const std::string &file_format,
- const std::string &record_format) {
- // Check file format.
- if (input.file_format_size() > 0) {
- bool found = false;
- for (int i = 0; i < input.file_format_size(); ++i) {
- if (input.file_format(i) == file_format) {
- found = true;
- break;
- }
- }
- if (!found) return false;
- }
-
- // Check record format.
- if (input.record_format_size() > 0) {
- bool found = false;
- for (int i = 0; i < input.record_format_size(); ++i) {
- if (input.record_format(i) == record_format) {
- found = true;
- break;
- }
- }
- if (!found) return false;
- }
-
- return true;
-}
-
-} // namespace nlp_core
-} // namespace libtextclassifier
diff --git a/common/task-context.h b/common/task-context.h
deleted file mode 100644
index c55ed67..0000000
--- a/common/task-context.h
+++ /dev/null
@@ -1,87 +0,0 @@
-/*
- * Copyright (C) 2017 The Android Open Source Project
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#ifndef LIBTEXTCLASSIFIER_COMMON_TASK_CONTEXT_H_
-#define LIBTEXTCLASSIFIER_COMMON_TASK_CONTEXT_H_
-
-#include <string>
-#include <vector>
-
-#include "common/task-spec.pb.h"
-#include "util/base/integral_types.h"
-
-namespace libtextclassifier {
-namespace nlp_core {
-
-// A task context holds configuration information for a task. It is basically a
-// wrapper around a TaskSpec protocol buffer.
-class TaskContext {
- public:
- // Returns the underlying task specification protocol buffer for the context.
- const TaskSpec &spec() const { return spec_; }
- TaskSpec *mutable_spec() { return &spec_; }
-
- // Returns a named input descriptor for the task. A new input is created if
- // the task context does not already have an input with that name.
- TaskInput *GetInput(const std::string &name);
- TaskInput *GetInput(const std::string &name,
- const std::string &file_format,
- const std::string &record_format);
-
- // Sets task parameter.
- void SetParameter(const std::string &name, const std::string &value);
-
- // Returns task parameter. If the parameter is not in the task configuration
- // the (default) value of the corresponding command line flag is returned.
- std::string GetParameter(const std::string &name) const;
- int GetIntParameter(const std::string &name) const;
- int64 GetInt64Parameter(const std::string &name) const;
- bool GetBoolParameter(const std::string &name) const;
- double GetFloatParameter(const std::string &name) const;
-
- // Returns task parameter. If the parameter is not in the task configuration
- // the default value is returned.
- std::string Get(const std::string &name, const std::string &defval) const;
- std::string Get(const std::string &name, const char *defval) const;
- int Get(const std::string &name, int defval) const;
- int64 Get(const std::string &name, int64 defval) const;
- double Get(const std::string &name, double defval) const;
- bool Get(const std::string &name, bool defval) const;
-
- // Returns input file name for a single-file task input.
- //
- // Special cases: returns the empty string if the TaskInput does not have any
- // input files. Returns the first file if the TaskInput has multiple input
- // files.
- static std::string InputFile(const TaskInput &input);
-
- // Returns true if task input supports the file and record format.
- static bool Supports(const TaskInput &input, const std::string &file_format,
- const std::string &record_format);
-
- private:
- // Underlying task specification protocol buffer.
- TaskSpec spec_;
-
- // Vector of parameters required by this task. These must be specified in the
- // task rather than relying on default values.
- std::vector<std::string> required_parameters_;
-};
-
-} // namespace nlp_core
-} // namespace libtextclassifier
-
-#endif // LIBTEXTCLASSIFIER_COMMON_TASK_CONTEXT_H_
diff --git a/common/task-spec.proto b/common/task-spec.proto
deleted file mode 100644
index ab986ce..0000000
--- a/common/task-spec.proto
+++ /dev/null
@@ -1,61 +0,0 @@
-// Copyright (C) 2017 The Android Open Source Project
-//
-// Licensed under the Apache License, Version 2.0 (the "License");
-// you may not use this file except in compliance with the License.
-// You may obtain a copy of the License at
-//
-// http://www.apache.org/licenses/LICENSE-2.0
-//
-// Unless required by applicable law or agreed to in writing, software
-// distributed under the License is distributed on an "AS IS" BASIS,
-// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-// See the License for the specific language governing permissions and
-// limitations under the License.
-
-// LINT: ALLOW_GROUPS
-// Protocol buffer specifications for task configuration.
-
-syntax = "proto2";
-option optimize_for = LITE_RUNTIME;
-
-package libtextclassifier.nlp_core;
-
-// Task input descriptor.
-message TaskInput {
- // Name of input resource.
- required string name = 1;
-
- // File format for resource.
- repeated string file_format = 3;
-
- // Record format for resource.
- repeated string record_format = 4;
-
- // An input can consist of multiple file sets.
- repeated group Part = 6 {
- // File pattern for file set.
- optional string file_pattern = 7;
-
- // File format for file set.
- optional string file_format = 8;
-
- // Record format for file set.
- optional string record_format = 9;
- }
-
- reserved 2, 5;
-}
-
-// A task specification is used for describing executing parameters.
-message TaskSpec {
- // Task parameters.
- repeated group Parameter = 3 {
- required string name = 4;
- optional string value = 5;
- }
-
- // Task inputs.
- repeated TaskInput input = 6;
-
- reserved 1, 2, 7;
-}
diff --git a/common/vector-span.h b/common/vector-span.h
deleted file mode 100644
index d7fbfe9..0000000
--- a/common/vector-span.h
+++ /dev/null
@@ -1,50 +0,0 @@
-/*
- * Copyright (C) 2017 The Android Open Source Project
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#ifndef LIBTEXTCLASSIFIER_COMMON_VECTOR_SPAN_H_
-#define LIBTEXTCLASSIFIER_COMMON_VECTOR_SPAN_H_
-
-#include <vector>
-
-namespace libtextclassifier {
-
-// StringPiece analogue for std::vector<T>.
-template <class T>
-class VectorSpan {
- public:
- VectorSpan() : begin_(), end_() {}
- VectorSpan(const std::vector<T>& v) // NOLINT(runtime/explicit)
- : begin_(v.begin()), end_(v.end()) {}
- VectorSpan(typename std::vector<T>::const_iterator begin,
- typename std::vector<T>::const_iterator end)
- : begin_(begin), end_(end) {}
-
- const T& operator[](typename std::vector<T>::size_type i) const {
- return *(begin_ + i);
- }
-
- int size() const { return end_ - begin_; }
- typename std::vector<T>::const_iterator begin() const { return begin_; }
- typename std::vector<T>::const_iterator end() const { return end_; }
-
- private:
- typename std::vector<T>::const_iterator begin_;
- typename std::vector<T>::const_iterator end_;
-};
-
-} // namespace libtextclassifier
-
-#endif // LIBTEXTCLASSIFIER_COMMON_VECTOR_SPAN_H_
diff --git a/common/workspace.cc b/common/workspace.cc
deleted file mode 100644
index 770e4be..0000000
--- a/common/workspace.cc
+++ /dev/null
@@ -1,63 +0,0 @@
-/*
- * Copyright (C) 2017 The Android Open Source Project
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#include "common/workspace.h"
-
-#include <atomic>
-#include <string>
-
-namespace libtextclassifier {
-namespace nlp_core {
-
-// static
-int GetFreshTypeId() {
- // Static local below is initialized the first time this method is run.
- static std::atomic<int> counter(0);
- return counter++;
-}
-
-std::string WorkspaceRegistry::DebugString() const {
- std::string str;
- for (auto &it : workspace_names_) {
- const std::string &type_name = workspace_types_.at(it.first);
- for (size_t index = 0; index < it.second.size(); ++index) {
- const std::string &workspace_name = it.second[index];
- str.append("\n ");
- str.append(type_name);
- str.append(" :: ");
- str.append(workspace_name);
- }
- }
- return str;
-}
-
-VectorIntWorkspace::VectorIntWorkspace(int size) : elements_(size) {}
-
-VectorIntWorkspace::VectorIntWorkspace(int size, int value)
- : elements_(size, value) {}
-
-VectorIntWorkspace::VectorIntWorkspace(const std::vector<int> &elements)
- : elements_(elements) {}
-
-std::string VectorIntWorkspace::TypeName() { return "Vector"; }
-
-VectorVectorIntWorkspace::VectorVectorIntWorkspace(int size)
- : elements_(size) {}
-
-std::string VectorVectorIntWorkspace::TypeName() { return "VectorVector"; }
-
-} // namespace nlp_core
-} // namespace libtextclassifier
diff --git a/common/workspace.h b/common/workspace.h
deleted file mode 100644
index e003bde..0000000
--- a/common/workspace.h
+++ /dev/null
@@ -1,245 +0,0 @@
-/*
- * Copyright (C) 2017 The Android Open Source Project
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-// Notes on thread-safety: All of the classes here are thread-compatible. More
-// specifically, the registry machinery is thread-safe, as long as each thread
-// performs feature extraction on a different Sentence object.
-
-#ifndef LIBTEXTCLASSIFIER_COMMON_WORKSPACE_H_
-#define LIBTEXTCLASSIFIER_COMMON_WORKSPACE_H_
-
-#include <stddef.h>
-#include <string>
-#include <unordered_map>
-#include <utility>
-#include <vector>
-
-#include "util/base/logging.h"
-#include "util/base/macros.h"
-
-namespace libtextclassifier {
-namespace nlp_core {
-
-// A base class for shared workspaces. Derived classes implement a static member
-// function TypeName() which returns a human readable std::string name for the
-// class.
-class Workspace {
- public:
- // Polymorphic destructor.
- virtual ~Workspace() {}
-
- protected:
- // Create an empty workspace.
- Workspace() {}
-
- private:
- TC_DISALLOW_COPY_AND_ASSIGN(Workspace);
-};
-
-// Returns a new, strictly increasing int every time it is invoked.
-int GetFreshTypeId();
-
-// Struct to simulate typeid, but without RTTI.
-template <typename T>
-struct TypeId {
- static int type_id;
-};
-
-template <typename T>
-int TypeId<T>::type_id = GetFreshTypeId();
-
-// A registry that keeps track of workspaces.
-class WorkspaceRegistry {
- public:
- // Create an empty registry.
- WorkspaceRegistry() {}
-
- // Returns the index of a named workspace, adding it to the registry first
- // if necessary.
- template <class W>
- int Request(const std::string &name) {
- const int id = TypeId<W>::type_id;
- max_workspace_id_ = std::max(id, max_workspace_id_);
- workspace_types_[id] = W::TypeName();
- std::vector<std::string> &names = workspace_names_[id];
- for (int i = 0; i < names.size(); ++i) {
- if (names[i] == name) return i;
- }
- names.push_back(name);
- return names.size() - 1;
- }
-
- // Returns the maximum workspace id that has been registered.
- int MaxId() const {
- return max_workspace_id_;
- }
-
- const std::unordered_map<int, std::vector<std::string> > &WorkspaceNames()
- const {
- return workspace_names_;
- }
-
- // Returns a std::string describing the registered workspaces.
- std::string DebugString() const;
-
- private:
- // Workspace type names, indexed as workspace_types_[typeid].
- std::unordered_map<int, std::string> workspace_types_;
-
- // Workspace names, indexed as workspace_names_[typeid][workspace].
- std::unordered_map<int, std::vector<std::string> > workspace_names_;
-
- // The maximum workspace id that has been registered.
- int max_workspace_id_ = 0;
-
- TC_DISALLOW_COPY_AND_ASSIGN(WorkspaceRegistry);
-};
-
-// A typed collected of workspaces. The workspaces are indexed according to an
-// external WorkspaceRegistry. If the WorkspaceSet is const, the contents are
-// also immutable.
-class WorkspaceSet {
- public:
- ~WorkspaceSet() { Reset(WorkspaceRegistry()); }
-
- // Returns true if a workspace has been set.
- template <class W>
- bool Has(int index) const {
- const int id = TypeId<W>::type_id;
- TC_DCHECK_GE(id, 0);
- TC_DCHECK_LT(id, workspaces_.size());
- TC_DCHECK_GE(index, 0);
- TC_DCHECK_LT(index, workspaces_[id].size());
- if (id >= workspaces_.size()) return false;
- return workspaces_[id][index] != nullptr;
- }
-
- // Returns an indexed workspace; the workspace must have been set.
- template <class W>
- const W &Get(int index) const {
- TC_DCHECK(Has<W>(index));
- const int id = TypeId<W>::type_id;
- const Workspace *w = workspaces_[id][index];
- return reinterpret_cast<const W &>(*w);
- }
-
- // Sets an indexed workspace; this takes ownership of the workspace, which
- // must have been new-allocated. It is an error to set a workspace twice.
- template <class W>
- void Set(int index, W *workspace) {
- const int id = TypeId<W>::type_id;
- TC_DCHECK_GE(id, 0);
- TC_DCHECK_LT(id, workspaces_.size());
- TC_DCHECK_GE(index, 0);
- TC_DCHECK_LT(index, workspaces_[id].size());
- TC_DCHECK(workspaces_[id][index] == nullptr);
- TC_DCHECK(workspace != nullptr);
- workspaces_[id][index] = workspace;
- }
-
- void Reset(const WorkspaceRegistry ®istry) {
- // Deallocate current workspaces.
- for (auto &it : workspaces_) {
- for (size_t index = 0; index < it.size(); ++index) {
- delete it[index];
- }
- }
- workspaces_.clear();
- workspaces_.resize(registry.MaxId() + 1, std::vector<Workspace *>());
- for (auto &it : registry.WorkspaceNames()) {
- workspaces_[it.first].resize(it.second.size());
- }
- }
-
- private:
- // The set of workspaces, indexed as workspaces_[typeid][index].
- std::vector<std::vector<Workspace *> > workspaces_;
-};
-
-// A workspace that wraps around a single int.
-class SingletonIntWorkspace : public Workspace {
- public:
- // Default-initializes the int value.
- SingletonIntWorkspace() {}
-
- // Initializes the int with the given value.
- explicit SingletonIntWorkspace(int value) : value_(value) {}
-
- // Returns the name of this type of workspace.
- static std::string TypeName() { return "SingletonInt"; }
-
- // Returns the int value.
- int get() const { return value_; }
-
- // Sets the int value.
- void set(int value) { value_ = value; }
-
- private:
- // The enclosed int.
- int value_ = 0;
-};
-
-// A workspace that wraps around a vector of int.
-class VectorIntWorkspace : public Workspace {
- public:
- // Creates a vector of the given size.
- explicit VectorIntWorkspace(int size);
-
- // Creates a vector initialized with the given array.
- explicit VectorIntWorkspace(const std::vector<int> &elements);
-
- // Creates a vector of the given size, with each element initialized to the
- // given value.
- VectorIntWorkspace(int size, int value);
-
- // Returns the name of this type of workspace.
- static std::string TypeName();
-
- // Returns the i'th element.
- int element(int i) const { return elements_[i]; }
-
- // Sets the i'th element.
- void set_element(int i, int value) { elements_[i] = value; }
-
- private:
- // The enclosed vector.
- std::vector<int> elements_;
-};
-
-// A workspace that wraps around a vector of vector of int.
-class VectorVectorIntWorkspace : public Workspace {
- public:
- // Creates a vector of empty vectors of the given size.
- explicit VectorVectorIntWorkspace(int size);
-
- // Returns the name of this type of workspace.
- static std::string TypeName();
-
- // Returns the i'th vector of elements.
- const std::vector<int> &elements(int i) const { return elements_[i]; }
-
- // Mutable access to the i'th vector of elements.
- std::vector<int> *mutable_elements(int i) { return &(elements_[i]); }
-
- private:
- // The enclosed vector of vector of elements.
- std::vector<std::vector<int> > elements_;
-};
-
-} // namespace nlp_core
-} // namespace libtextclassifier
-
-#endif // LIBTEXTCLASSIFIER_COMMON_WORKSPACE_H_
diff --git a/datetime/extractor.cc b/datetime/extractor.cc
new file mode 100644
index 0000000..f4ab8f4
--- /dev/null
+++ b/datetime/extractor.cc
@@ -0,0 +1,469 @@
+/*
+ * Copyright (C) 2017 The Android Open Source Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "datetime/extractor.h"
+
+#include "util/base/logging.h"
+
+namespace libtextclassifier2 {
+
+bool DatetimeExtractor::Extract(DateParseData* result,
+ CodepointSpan* result_span) const {
+ result->field_set_mask = 0;
+ *result_span = {kInvalidIndex, kInvalidIndex};
+
+ if (rule_.regex->groups() == nullptr) {
+ return false;
+ }
+
+ for (int group_id = 0; group_id < rule_.regex->groups()->size(); group_id++) {
+ UnicodeText group_text;
+ const int group_type = rule_.regex->groups()->Get(group_id);
+ if (group_type == DatetimeGroupType_GROUP_UNUSED) {
+ continue;
+ }
+ if (!GroupTextFromMatch(group_id, &group_text)) {
+ TC_LOG(ERROR) << "Couldn't retrieve group.";
+ return false;
+ }
+ // The pattern can have a group defined in a part that was not matched,
+ // e.g. an optional part. In this case we'll get an empty content here.
+ if (group_text.empty()) {
+ continue;
+ }
+ switch (group_type) {
+ case DatetimeGroupType_GROUP_YEAR: {
+ if (!ParseYear(group_text, &(result->year))) {
+ TC_LOG(ERROR) << "Couldn't extract YEAR.";
+ return false;
+ }
+ result->field_set_mask |= DateParseData::YEAR_FIELD;
+ break;
+ }
+ case DatetimeGroupType_GROUP_MONTH: {
+ if (!ParseMonth(group_text, &(result->month))) {
+ TC_LOG(ERROR) << "Couldn't extract MONTH.";
+ return false;
+ }
+ result->field_set_mask |= DateParseData::MONTH_FIELD;
+ break;
+ }
+ case DatetimeGroupType_GROUP_DAY: {
+ if (!ParseDigits(group_text, &(result->day_of_month))) {
+ TC_LOG(ERROR) << "Couldn't extract DAY.";
+ return false;
+ }
+ result->field_set_mask |= DateParseData::DAY_FIELD;
+ break;
+ }
+ case DatetimeGroupType_GROUP_HOUR: {
+ if (!ParseDigits(group_text, &(result->hour))) {
+ TC_LOG(ERROR) << "Couldn't extract HOUR.";
+ return false;
+ }
+ result->field_set_mask |= DateParseData::HOUR_FIELD;
+ break;
+ }
+ case DatetimeGroupType_GROUP_MINUTE: {
+ if (!ParseDigits(group_text, &(result->minute))) {
+ TC_LOG(ERROR) << "Couldn't extract MINUTE.";
+ return false;
+ }
+ result->field_set_mask |= DateParseData::MINUTE_FIELD;
+ break;
+ }
+ case DatetimeGroupType_GROUP_SECOND: {
+ if (!ParseDigits(group_text, &(result->second))) {
+ TC_LOG(ERROR) << "Couldn't extract SECOND.";
+ return false;
+ }
+ result->field_set_mask |= DateParseData::SECOND_FIELD;
+ break;
+ }
+ case DatetimeGroupType_GROUP_AMPM: {
+ if (!ParseAMPM(group_text, &(result->ampm))) {
+ TC_LOG(ERROR) << "Couldn't extract AMPM.";
+ return false;
+ }
+ result->field_set_mask |= DateParseData::AMPM_FIELD;
+ break;
+ }
+ case DatetimeGroupType_GROUP_RELATIONDISTANCE: {
+ if (!ParseRelationDistance(group_text, &(result->relation_distance))) {
+ TC_LOG(ERROR) << "Couldn't extract RELATION_DISTANCE_FIELD.";
+ return false;
+ }
+ result->field_set_mask |= DateParseData::RELATION_DISTANCE_FIELD;
+ break;
+ }
+ case DatetimeGroupType_GROUP_RELATION: {
+ if (!ParseRelation(group_text, &(result->relation))) {
+ TC_LOG(ERROR) << "Couldn't extract RELATION_FIELD.";
+ return false;
+ }
+ result->field_set_mask |= DateParseData::RELATION_FIELD;
+ break;
+ }
+ case DatetimeGroupType_GROUP_RELATIONTYPE: {
+ if (!ParseRelationType(group_text, &(result->relation_type))) {
+ TC_LOG(ERROR) << "Couldn't extract RELATION_TYPE_FIELD.";
+ return false;
+ }
+ result->field_set_mask |= DateParseData::RELATION_TYPE_FIELD;
+ break;
+ }
+ case DatetimeGroupType_GROUP_DUMMY1:
+ case DatetimeGroupType_GROUP_DUMMY2:
+ break;
+ default:
+ TC_LOG(INFO) << "Unknown group type.";
+ continue;
+ }
+ if (!UpdateMatchSpan(group_id, result_span)) {
+ TC_LOG(ERROR) << "Couldn't update span.";
+ return false;
+ }
+ }
+
+ if (result_span->first == kInvalidIndex ||
+ result_span->second == kInvalidIndex) {
+ *result_span = {kInvalidIndex, kInvalidIndex};
+ }
+
+ return true;
+}
+
+bool DatetimeExtractor::RuleIdForType(DatetimeExtractorType type,
+ int* rule_id) const {
+ auto type_it = type_and_locale_to_rule_.find(type);
+ if (type_it == type_and_locale_to_rule_.end()) {
+ return false;
+ }
+
+ auto locale_it = type_it->second.find(locale_id_);
+ if (locale_it == type_it->second.end()) {
+ return false;
+ }
+ *rule_id = locale_it->second;
+ return true;
+}
+
+bool DatetimeExtractor::ExtractType(const UnicodeText& input,
+ DatetimeExtractorType extractor_type,
+ UnicodeText* match_result) const {
+ int rule_id;
+ if (!RuleIdForType(extractor_type, &rule_id)) {
+ return false;
+ }
+
+ std::unique_ptr<UniLib::RegexMatcher> matcher =
+ rules_[rule_id]->Matcher(input);
+ if (!matcher) {
+ return false;
+ }
+
+ int status;
+ if (!matcher->Find(&status)) {
+ return false;
+ }
+
+ if (match_result != nullptr) {
+ *match_result = matcher->Group(&status);
+ if (status != UniLib::RegexMatcher::kNoError) {
+ return false;
+ }
+ }
+ return true;
+}
+
+bool DatetimeExtractor::GroupTextFromMatch(int group_id,
+ UnicodeText* result) const {
+ int status;
+ *result = matcher_.Group(group_id, &status);
+ if (status != UniLib::RegexMatcher::kNoError) {
+ return false;
+ }
+ return true;
+}
+
+bool DatetimeExtractor::UpdateMatchSpan(int group_id,
+ CodepointSpan* span) const {
+ int status;
+ const int match_start = matcher_.Start(group_id, &status);
+ if (status != UniLib::RegexMatcher::kNoError) {
+ return false;
+ }
+ const int match_end = matcher_.End(group_id, &status);
+ if (status != UniLib::RegexMatcher::kNoError) {
+ return false;
+ }
+ if (span->first == kInvalidIndex || span->first > match_start) {
+ span->first = match_start;
+ }
+ if (span->second == kInvalidIndex || span->second < match_end) {
+ span->second = match_end;
+ }
+
+ return true;
+}
+
+template <typename T>
+bool DatetimeExtractor::MapInput(
+ const UnicodeText& input,
+ const std::vector<std::pair<DatetimeExtractorType, T>>& mapping,
+ T* result) const {
+ for (const auto& type_value_pair : mapping) {
+ if (ExtractType(input, type_value_pair.first)) {
+ *result = type_value_pair.second;
+ return true;
+ }
+ }
+ return false;
+}
+
+bool DatetimeExtractor::ParseWrittenNumber(const UnicodeText& input,
+ int* parsed_number) const {
+ std::vector<std::pair<int, int>> found_numbers;
+ for (const auto& type_value_pair :
+ std::vector<std::pair<DatetimeExtractorType, int>>{
+ {DatetimeExtractorType_ZERO, 0},
+ {DatetimeExtractorType_ONE, 1},
+ {DatetimeExtractorType_TWO, 2},
+ {DatetimeExtractorType_THREE, 3},
+ {DatetimeExtractorType_FOUR, 4},
+ {DatetimeExtractorType_FIVE, 5},
+ {DatetimeExtractorType_SIX, 6},
+ {DatetimeExtractorType_SEVEN, 7},
+ {DatetimeExtractorType_EIGHT, 8},
+ {DatetimeExtractorType_NINE, 9},
+ {DatetimeExtractorType_TEN, 10},
+ {DatetimeExtractorType_ELEVEN, 11},
+ {DatetimeExtractorType_TWELVE, 12},
+ {DatetimeExtractorType_THIRTEEN, 13},
+ {DatetimeExtractorType_FOURTEEN, 14},
+ {DatetimeExtractorType_FIFTEEN, 15},
+ {DatetimeExtractorType_SIXTEEN, 16},
+ {DatetimeExtractorType_SEVENTEEN, 17},
+ {DatetimeExtractorType_EIGHTEEN, 18},
+ {DatetimeExtractorType_NINETEEN, 19},
+ {DatetimeExtractorType_TWENTY, 20},
+ {DatetimeExtractorType_THIRTY, 30},
+ {DatetimeExtractorType_FORTY, 40},
+ {DatetimeExtractorType_FIFTY, 50},
+ {DatetimeExtractorType_SIXTY, 60},
+ {DatetimeExtractorType_SEVENTY, 70},
+ {DatetimeExtractorType_EIGHTY, 80},
+ {DatetimeExtractorType_NINETY, 90},
+ {DatetimeExtractorType_HUNDRED, 100},
+ {DatetimeExtractorType_THOUSAND, 1000},
+ }) {
+ int rule_id;
+ if (!RuleIdForType(type_value_pair.first, &rule_id)) {
+ return false;
+ }
+
+ std::unique_ptr<UniLib::RegexMatcher> matcher =
+ rules_[rule_id]->Matcher(input);
+ if (!matcher) {
+ return false;
+ }
+
+ int status;
+ while (matcher->Find(&status) && status == UniLib::RegexMatcher::kNoError) {
+ int span_start = matcher->Start(&status);
+ if (status != UniLib::RegexMatcher::kNoError) {
+ return false;
+ }
+ found_numbers.push_back({span_start, type_value_pair.second});
+ }
+ }
+
+ std::sort(found_numbers.begin(), found_numbers.end(),
+ [](const std::pair<int, int>& a, const std::pair<int, int>& b) {
+ return a.first < b.first;
+ });
+
+ int sum = 0;
+ int running_value = -1;
+ // Simple math to make sure we handle written numerical modifiers correctly
+ // so that :="fifty one thousand and one" maps to 51001 and not 50 1 1000 1.
+ for (const std::pair<int, int> position_number_pair : found_numbers) {
+ if (running_value >= 0) {
+ if (running_value > position_number_pair.second) {
+ sum += running_value;
+ running_value = position_number_pair.second;
+ } else {
+ running_value *= position_number_pair.second;
+ }
+ } else {
+ running_value = position_number_pair.second;
+ }
+ }
+ sum += running_value;
+ *parsed_number = sum;
+ return true;
+}
+
+bool DatetimeExtractor::ParseDigits(const UnicodeText& input,
+ int* parsed_digits) const {
+ UnicodeText digit;
+ if (!ExtractType(input, DatetimeExtractorType_DIGITS, &digit)) {
+ return false;
+ }
+
+ if (!unilib_.ParseInt32(digit, parsed_digits)) {
+ return false;
+ }
+ return true;
+}
+
+bool DatetimeExtractor::ParseYear(const UnicodeText& input,
+ int* parsed_year) const {
+ if (!ParseDigits(input, parsed_year)) {
+ return false;
+ }
+
+ if (*parsed_year < 100) {
+ if (*parsed_year < 50) {
+ *parsed_year += 2000;
+ } else {
+ *parsed_year += 1900;
+ }
+ }
+
+ return true;
+}
+
+bool DatetimeExtractor::ParseMonth(const UnicodeText& input,
+ int* parsed_month) const {
+ if (ParseDigits(input, parsed_month)) {
+ return true;
+ }
+
+ if (MapInput(input,
+ {
+ {DatetimeExtractorType_JANUARY, 1},
+ {DatetimeExtractorType_FEBRUARY, 2},
+ {DatetimeExtractorType_MARCH, 3},
+ {DatetimeExtractorType_APRIL, 4},
+ {DatetimeExtractorType_MAY, 5},
+ {DatetimeExtractorType_JUNE, 6},
+ {DatetimeExtractorType_JULY, 7},
+ {DatetimeExtractorType_AUGUST, 8},
+ {DatetimeExtractorType_SEPTEMBER, 9},
+ {DatetimeExtractorType_OCTOBER, 10},
+ {DatetimeExtractorType_NOVEMBER, 11},
+ {DatetimeExtractorType_DECEMBER, 12},
+ },
+ parsed_month)) {
+ return true;
+ }
+
+ return false;
+}
+
+bool DatetimeExtractor::ParseAMPM(const UnicodeText& input,
+ int* parsed_ampm) const {
+ return MapInput(input,
+ {
+ {DatetimeExtractorType_AM, DateParseData::AMPM::AM},
+ {DatetimeExtractorType_PM, DateParseData::AMPM::PM},
+ },
+ parsed_ampm);
+}
+
+bool DatetimeExtractor::ParseRelationDistance(const UnicodeText& input,
+ int* parsed_distance) const {
+ if (ParseDigits(input, parsed_distance)) {
+ return true;
+ }
+ if (ParseWrittenNumber(input, parsed_distance)) {
+ return true;
+ }
+ return false;
+}
+
+bool DatetimeExtractor::ParseRelation(
+ const UnicodeText& input, DateParseData::Relation* parsed_relation) const {
+ return MapInput(
+ input,
+ {
+ {DatetimeExtractorType_NOW, DateParseData::Relation::NOW},
+ {DatetimeExtractorType_YESTERDAY, DateParseData::Relation::YESTERDAY},
+ {DatetimeExtractorType_TOMORROW, DateParseData::Relation::TOMORROW},
+ {DatetimeExtractorType_NEXT, DateParseData::Relation::NEXT},
+ {DatetimeExtractorType_NEXT_OR_SAME,
+ DateParseData::Relation::NEXT_OR_SAME},
+ {DatetimeExtractorType_LAST, DateParseData::Relation::LAST},
+ {DatetimeExtractorType_PAST, DateParseData::Relation::PAST},
+ {DatetimeExtractorType_FUTURE, DateParseData::Relation::FUTURE},
+ },
+ parsed_relation);
+}
+
+bool DatetimeExtractor::ParseRelationType(
+ const UnicodeText& input,
+ DateParseData::RelationType* parsed_relation_type) const {
+ return MapInput(
+ input,
+ {
+ {DatetimeExtractorType_MONDAY, DateParseData::MONDAY},
+ {DatetimeExtractorType_TUESDAY, DateParseData::TUESDAY},
+ {DatetimeExtractorType_WEDNESDAY, DateParseData::WEDNESDAY},
+ {DatetimeExtractorType_THURSDAY, DateParseData::THURSDAY},
+ {DatetimeExtractorType_FRIDAY, DateParseData::FRIDAY},
+ {DatetimeExtractorType_SATURDAY, DateParseData::SATURDAY},
+ {DatetimeExtractorType_SUNDAY, DateParseData::SUNDAY},
+ {DatetimeExtractorType_DAY, DateParseData::DAY},
+ {DatetimeExtractorType_WEEK, DateParseData::WEEK},
+ {DatetimeExtractorType_MONTH, DateParseData::MONTH},
+ {DatetimeExtractorType_YEAR, DateParseData::YEAR},
+ },
+ parsed_relation_type);
+}
+
+bool DatetimeExtractor::ParseTimeUnit(const UnicodeText& input,
+ int* parsed_time_unit) const {
+ return MapInput(input,
+ {
+ {DatetimeExtractorType_DAYS, DateParseData::DAYS},
+ {DatetimeExtractorType_WEEKS, DateParseData::WEEKS},
+ {DatetimeExtractorType_MONTHS, DateParseData::MONTHS},
+ {DatetimeExtractorType_HOURS, DateParseData::HOURS},
+ {DatetimeExtractorType_MINUTES, DateParseData::MINUTES},
+ {DatetimeExtractorType_SECONDS, DateParseData::SECONDS},
+ {DatetimeExtractorType_YEARS, DateParseData::YEARS},
+ },
+ parsed_time_unit);
+}
+
+bool DatetimeExtractor::ParseWeekday(const UnicodeText& input,
+ int* parsed_weekday) const {
+ return MapInput(
+ input,
+ {
+ {DatetimeExtractorType_MONDAY, DateParseData::MONDAY},
+ {DatetimeExtractorType_TUESDAY, DateParseData::TUESDAY},
+ {DatetimeExtractorType_WEDNESDAY, DateParseData::WEDNESDAY},
+ {DatetimeExtractorType_THURSDAY, DateParseData::THURSDAY},
+ {DatetimeExtractorType_FRIDAY, DateParseData::FRIDAY},
+ {DatetimeExtractorType_SATURDAY, DateParseData::SATURDAY},
+ {DatetimeExtractorType_SUNDAY, DateParseData::SUNDAY},
+ },
+ parsed_weekday);
+}
+
+} // namespace libtextclassifier2
diff --git a/datetime/extractor.h b/datetime/extractor.h
new file mode 100644
index 0000000..5c36ec4
--- /dev/null
+++ b/datetime/extractor.h
@@ -0,0 +1,111 @@
+/*
+ * Copyright (C) 2017 The Android Open Source Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#ifndef LIBTEXTCLASSIFIER_DATETIME_EXTRACTOR_H_
+#define LIBTEXTCLASSIFIER_DATETIME_EXTRACTOR_H_
+
+#include <string>
+#include <unordered_map>
+#include <vector>
+
+#include "model_generated.h"
+#include "types.h"
+#include "util/strings/stringpiece.h"
+#include "util/utf8/unicodetext.h"
+#include "util/utf8/unilib.h"
+
+namespace libtextclassifier2 {
+
+struct CompiledRule {
+ // The compiled regular expression.
+ std::unique_ptr<const UniLib::RegexPattern> compiled_regex;
+
+ // The uncompiled pattern and information about the pattern groups.
+ const DatetimeModelPattern_::Regex* regex;
+
+ // DatetimeModelPattern which 'regex' is part of and comes from.
+ const DatetimeModelPattern* pattern;
+};
+
+// A helper class for DatetimeParser that extracts structured data
+// (DateParseDate) from the current match of the passed RegexMatcher.
+class DatetimeExtractor {
+ public:
+ DatetimeExtractor(
+ const CompiledRule& rule, const UniLib::RegexMatcher& matcher,
+ int locale_id, const UniLib& unilib,
+ const std::vector<std::unique_ptr<const UniLib::RegexPattern>>&
+ extractor_rules,
+ const std::unordered_map<DatetimeExtractorType,
+ std::unordered_map<int, int>>&
+ type_and_locale_to_extractor_rule)
+ : rule_(rule),
+ matcher_(matcher),
+ locale_id_(locale_id),
+ unilib_(unilib),
+ rules_(extractor_rules),
+ type_and_locale_to_rule_(type_and_locale_to_extractor_rule) {}
+ bool Extract(DateParseData* result, CodepointSpan* result_span) const;
+
+ private:
+ bool RuleIdForType(DatetimeExtractorType type, int* rule_id) const;
+
+ // Returns true if the rule for given extractor matched. If it matched,
+ // match_result will contain the first group of the rule (if match_result not
+ // nullptr).
+ bool ExtractType(const UnicodeText& input,
+ DatetimeExtractorType extractor_type,
+ UnicodeText* match_result = nullptr) const;
+
+ bool GroupTextFromMatch(int group_id, UnicodeText* result) const;
+
+ // Updates the span to include the current match for the given group.
+ bool UpdateMatchSpan(int group_id, CodepointSpan* span) const;
+
+ // Returns true if any of the extractors from 'mapping' matched. If it did,
+ // will fill 'result' with the associated value from 'mapping'.
+ template <typename T>
+ bool MapInput(const UnicodeText& input,
+ const std::vector<std::pair<DatetimeExtractorType, T>>& mapping,
+ T* result) const;
+
+ bool ParseDigits(const UnicodeText& input, int* parsed_digits) const;
+ bool ParseWrittenNumber(const UnicodeText& input, int* parsed_number) const;
+ bool ParseYear(const UnicodeText& input, int* parsed_year) const;
+ bool ParseMonth(const UnicodeText& input, int* parsed_month) const;
+ bool ParseAMPM(const UnicodeText& input, int* parsed_ampm) const;
+ bool ParseRelation(const UnicodeText& input,
+ DateParseData::Relation* parsed_relation) const;
+ bool ParseRelationDistance(const UnicodeText& input,
+ int* parsed_distance) const;
+ bool ParseTimeUnit(const UnicodeText& input, int* parsed_time_unit) const;
+ bool ParseRelationType(
+ const UnicodeText& input,
+ DateParseData::RelationType* parsed_relation_type) const;
+ bool ParseWeekday(const UnicodeText& input, int* parsed_weekday) const;
+
+ const CompiledRule& rule_;
+ const UniLib::RegexMatcher& matcher_;
+ int locale_id_;
+ const UniLib& unilib_;
+ const std::vector<std::unique_ptr<const UniLib::RegexPattern>>& rules_;
+ const std::unordered_map<DatetimeExtractorType, std::unordered_map<int, int>>&
+ type_and_locale_to_rule_;
+};
+
+} // namespace libtextclassifier2
+
+#endif // LIBTEXTCLASSIFIER_DATETIME_EXTRACTOR_H_
diff --git a/datetime/parser.cc b/datetime/parser.cc
new file mode 100644
index 0000000..4bc5dff
--- /dev/null
+++ b/datetime/parser.cc
@@ -0,0 +1,405 @@
+/*
+ * Copyright (C) 2017 The Android Open Source Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "datetime/parser.h"
+
+#include <set>
+#include <unordered_set>
+
+#include "datetime/extractor.h"
+#include "util/calendar/calendar.h"
+#include "util/i18n/locale.h"
+#include "util/strings/split.h"
+
+namespace libtextclassifier2 {
+std::unique_ptr<DatetimeParser> DatetimeParser::Instance(
+ const DatetimeModel* model, const UniLib& unilib,
+ ZlibDecompressor* decompressor) {
+ std::unique_ptr<DatetimeParser> result(
+ new DatetimeParser(model, unilib, decompressor));
+ if (!result->initialized_) {
+ result.reset();
+ }
+ return result;
+}
+
+DatetimeParser::DatetimeParser(const DatetimeModel* model, const UniLib& unilib,
+ ZlibDecompressor* decompressor)
+ : unilib_(unilib) {
+ initialized_ = false;
+
+ if (model == nullptr) {
+ return;
+ }
+
+ if (model->patterns() != nullptr) {
+ for (const DatetimeModelPattern* pattern : *model->patterns()) {
+ if (pattern->regexes()) {
+ for (const DatetimeModelPattern_::Regex* regex : *pattern->regexes()) {
+ std::unique_ptr<UniLib::RegexPattern> regex_pattern =
+ UncompressMakeRegexPattern(unilib, regex->pattern(),
+ regex->compressed_pattern(),
+ decompressor);
+ if (!regex_pattern) {
+ TC_LOG(ERROR) << "Couldn't create rule pattern.";
+ return;
+ }
+ rules_.push_back({std::move(regex_pattern), regex, pattern});
+ if (pattern->locales()) {
+ for (int locale : *pattern->locales()) {
+ locale_to_rules_[locale].push_back(rules_.size() - 1);
+ }
+ }
+ }
+ }
+ }
+ }
+
+ if (model->extractors() != nullptr) {
+ for (const DatetimeModelExtractor* extractor : *model->extractors()) {
+ std::unique_ptr<UniLib::RegexPattern> regex_pattern =
+ UncompressMakeRegexPattern(unilib, extractor->pattern(),
+ extractor->compressed_pattern(),
+ decompressor);
+ if (!regex_pattern) {
+ TC_LOG(ERROR) << "Couldn't create extractor pattern";
+ return;
+ }
+ extractor_rules_.push_back(std::move(regex_pattern));
+
+ if (extractor->locales()) {
+ for (int locale : *extractor->locales()) {
+ type_and_locale_to_extractor_rule_[extractor->extractor()][locale] =
+ extractor_rules_.size() - 1;
+ }
+ }
+ }
+ }
+
+ if (model->locales() != nullptr) {
+ for (int i = 0; i < model->locales()->Length(); ++i) {
+ locale_string_to_id_[model->locales()->Get(i)->str()] = i;
+ }
+ }
+
+ if (model->default_locales() != nullptr) {
+ for (const int locale : *model->default_locales()) {
+ default_locale_ids_.push_back(locale);
+ }
+ }
+
+ use_extractors_for_locating_ = model->use_extractors_for_locating();
+
+ initialized_ = true;
+}
+
+bool DatetimeParser::Parse(
+ const std::string& input, const int64 reference_time_ms_utc,
+ const std::string& reference_timezone, const std::string& locales,
+ ModeFlag mode, bool anchor_start_end,
+ std::vector<DatetimeParseResultSpan>* results) const {
+ return Parse(UTF8ToUnicodeText(input, /*do_copy=*/false),
+ reference_time_ms_utc, reference_timezone, locales, mode,
+ anchor_start_end, results);
+}
+
+bool DatetimeParser::FindSpansUsingLocales(
+ const std::vector<int>& locale_ids, const UnicodeText& input,
+ const int64 reference_time_ms_utc, const std::string& reference_timezone,
+ ModeFlag mode, bool anchor_start_end, const std::string& reference_locale,
+ std::unordered_set<int>* executed_rules,
+ std::vector<DatetimeParseResultSpan>* found_spans) const {
+ for (const int locale_id : locale_ids) {
+ auto rules_it = locale_to_rules_.find(locale_id);
+ if (rules_it == locale_to_rules_.end()) {
+ continue;
+ }
+
+ for (const int rule_id : rules_it->second) {
+ // Skip rules that were already executed in previous locales.
+ if (executed_rules->find(rule_id) != executed_rules->end()) {
+ continue;
+ }
+
+ if (!(rules_[rule_id].pattern->enabled_modes() & mode)) {
+ continue;
+ }
+
+ executed_rules->insert(rule_id);
+
+ if (!ParseWithRule(rules_[rule_id], input, reference_time_ms_utc,
+ reference_timezone, reference_locale, locale_id,
+ anchor_start_end, found_spans)) {
+ return false;
+ }
+ }
+ }
+ return true;
+}
+
+bool DatetimeParser::Parse(
+ const UnicodeText& input, const int64 reference_time_ms_utc,
+ const std::string& reference_timezone, const std::string& locales,
+ ModeFlag mode, bool anchor_start_end,
+ std::vector<DatetimeParseResultSpan>* results) const {
+ std::vector<DatetimeParseResultSpan> found_spans;
+ std::unordered_set<int> executed_rules;
+ std::string reference_locale;
+ const std::vector<int> requested_locales =
+ ParseAndExpandLocales(locales, &reference_locale);
+ if (!FindSpansUsingLocales(requested_locales, input, reference_time_ms_utc,
+ reference_timezone, mode, anchor_start_end,
+ reference_locale, &executed_rules, &found_spans)) {
+ return false;
+ }
+
+ std::vector<std::pair<DatetimeParseResultSpan, int>> indexed_found_spans;
+ int counter = 0;
+ for (const auto& found_span : found_spans) {
+ indexed_found_spans.push_back({found_span, counter});
+ counter++;
+ }
+
+ // Resolve conflicts by always picking the longer span and breaking ties by
+ // selecting the earlier entry in the list for a given locale.
+ std::sort(indexed_found_spans.begin(), indexed_found_spans.end(),
+ [](const std::pair<DatetimeParseResultSpan, int>& a,
+ const std::pair<DatetimeParseResultSpan, int>& b) {
+ if ((a.first.span.second - a.first.span.first) !=
+ (b.first.span.second - b.first.span.first)) {
+ return (a.first.span.second - a.first.span.first) >
+ (b.first.span.second - b.first.span.first);
+ } else {
+ return a.second < b.second;
+ }
+ });
+
+ found_spans.clear();
+ for (auto& span_index_pair : indexed_found_spans) {
+ found_spans.push_back(span_index_pair.first);
+ }
+
+ std::set<int, std::function<bool(int, int)>> chosen_indices_set(
+ [&found_spans](int a, int b) {
+ return found_spans[a].span.first < found_spans[b].span.first;
+ });
+ for (int i = 0; i < found_spans.size(); ++i) {
+ if (!DoesCandidateConflict(i, found_spans, chosen_indices_set)) {
+ chosen_indices_set.insert(i);
+ results->push_back(found_spans[i]);
+ }
+ }
+
+ return true;
+}
+
+bool DatetimeParser::HandleParseMatch(
+ const CompiledRule& rule, const UniLib::RegexMatcher& matcher,
+ int64 reference_time_ms_utc, const std::string& reference_timezone,
+ const std::string& reference_locale, int locale_id,
+ std::vector<DatetimeParseResultSpan>* result) const {
+ int status = UniLib::RegexMatcher::kNoError;
+ const int start = matcher.Start(&status);
+ if (status != UniLib::RegexMatcher::kNoError) {
+ return false;
+ }
+
+ const int end = matcher.End(&status);
+ if (status != UniLib::RegexMatcher::kNoError) {
+ return false;
+ }
+
+ DatetimeParseResultSpan parse_result;
+ if (!ExtractDatetime(rule, matcher, reference_time_ms_utc, reference_timezone,
+ reference_locale, locale_id, &(parse_result.data),
+ &parse_result.span)) {
+ return false;
+ }
+ if (!use_extractors_for_locating_) {
+ parse_result.span = {start, end};
+ }
+ if (parse_result.span.first != kInvalidIndex &&
+ parse_result.span.second != kInvalidIndex) {
+ parse_result.target_classification_score =
+ rule.pattern->target_classification_score();
+ parse_result.priority_score = rule.pattern->priority_score();
+ result->push_back(parse_result);
+ }
+ return true;
+}
+
+bool DatetimeParser::ParseWithRule(
+ const CompiledRule& rule, const UnicodeText& input,
+ const int64 reference_time_ms_utc, const std::string& reference_timezone,
+ const std::string& reference_locale, const int locale_id,
+ bool anchor_start_end, std::vector<DatetimeParseResultSpan>* result) const {
+ std::unique_ptr<UniLib::RegexMatcher> matcher =
+ rule.compiled_regex->Matcher(input);
+ int status = UniLib::RegexMatcher::kNoError;
+ if (anchor_start_end) {
+ if (matcher->Matches(&status) && status == UniLib::RegexMatcher::kNoError) {
+ if (!HandleParseMatch(rule, *matcher, reference_time_ms_utc,
+ reference_timezone, reference_locale, locale_id,
+ result)) {
+ return false;
+ }
+ }
+ } else {
+ while (matcher->Find(&status) && status == UniLib::RegexMatcher::kNoError) {
+ if (!HandleParseMatch(rule, *matcher, reference_time_ms_utc,
+ reference_timezone, reference_locale, locale_id,
+ result)) {
+ return false;
+ }
+ }
+ }
+ return true;
+}
+
+std::vector<int> DatetimeParser::ParseAndExpandLocales(
+ const std::string& locales, std::string* reference_locale) const {
+ std::vector<StringPiece> split_locales = strings::Split(locales, ',');
+ if (!split_locales.empty()) {
+ *reference_locale = split_locales[0].ToString();
+ } else {
+ *reference_locale = "";
+ }
+
+ std::vector<int> result;
+ for (const StringPiece& locale_str : split_locales) {
+ auto locale_it = locale_string_to_id_.find(locale_str.ToString());
+ if (locale_it != locale_string_to_id_.end()) {
+ result.push_back(locale_it->second);
+ }
+
+ const Locale locale = Locale::FromBCP47(locale_str.ToString());
+ if (!locale.IsValid()) {
+ continue;
+ }
+
+ const std::string language = locale.Language();
+ const std::string script = locale.Script();
+ const std::string region = locale.Region();
+
+ // First, try adding *-region locale.
+ if (!region.empty()) {
+ locale_it = locale_string_to_id_.find("*-" + region);
+ if (locale_it != locale_string_to_id_.end()) {
+ result.push_back(locale_it->second);
+ }
+ }
+ // Second, try adding language-script-* locale.
+ if (!script.empty()) {
+ locale_it = locale_string_to_id_.find(language + "-" + script + "-*");
+ if (locale_it != locale_string_to_id_.end()) {
+ result.push_back(locale_it->second);
+ }
+ }
+ // Third, try adding language-* locale.
+ if (!language.empty()) {
+ locale_it = locale_string_to_id_.find(language + "-*");
+ if (locale_it != locale_string_to_id_.end()) {
+ result.push_back(locale_it->second);
+ }
+ }
+ }
+
+ // Add the default locales if they haven't been added already.
+ const std::unordered_set<int> result_set(result.begin(), result.end());
+ for (const int default_locale_id : default_locale_ids_) {
+ if (result_set.find(default_locale_id) == result_set.end()) {
+ result.push_back(default_locale_id);
+ }
+ }
+
+ return result;
+}
+
+namespace {
+
+DatetimeGranularity GetGranularity(const DateParseData& data) {
+ DatetimeGranularity granularity = DatetimeGranularity::GRANULARITY_YEAR;
+ if ((data.field_set_mask & DateParseData::YEAR_FIELD) ||
+ (data.field_set_mask & DateParseData::RELATION_TYPE_FIELD &&
+ (data.relation_type == DateParseData::RelationType::YEAR))) {
+ granularity = DatetimeGranularity::GRANULARITY_YEAR;
+ }
+ if ((data.field_set_mask & DateParseData::MONTH_FIELD) ||
+ (data.field_set_mask & DateParseData::RELATION_TYPE_FIELD &&
+ (data.relation_type == DateParseData::RelationType::MONTH))) {
+ granularity = DatetimeGranularity::GRANULARITY_MONTH;
+ }
+ if (data.field_set_mask & DateParseData::RELATION_TYPE_FIELD &&
+ (data.relation_type == DateParseData::RelationType::WEEK)) {
+ granularity = DatetimeGranularity::GRANULARITY_WEEK;
+ }
+ if (data.field_set_mask & DateParseData::DAY_FIELD ||
+ (data.field_set_mask & DateParseData::RELATION_FIELD &&
+ (data.relation == DateParseData::Relation::NOW ||
+ data.relation == DateParseData::Relation::TOMORROW ||
+ data.relation == DateParseData::Relation::YESTERDAY)) ||
+ (data.field_set_mask & DateParseData::RELATION_TYPE_FIELD &&
+ (data.relation_type == DateParseData::RelationType::MONDAY ||
+ data.relation_type == DateParseData::RelationType::TUESDAY ||
+ data.relation_type == DateParseData::RelationType::WEDNESDAY ||
+ data.relation_type == DateParseData::RelationType::THURSDAY ||
+ data.relation_type == DateParseData::RelationType::FRIDAY ||
+ data.relation_type == DateParseData::RelationType::SATURDAY ||
+ data.relation_type == DateParseData::RelationType::SUNDAY ||
+ data.relation_type == DateParseData::RelationType::DAY))) {
+ granularity = DatetimeGranularity::GRANULARITY_DAY;
+ }
+ if (data.field_set_mask & DateParseData::HOUR_FIELD) {
+ granularity = DatetimeGranularity::GRANULARITY_HOUR;
+ }
+ if (data.field_set_mask & DateParseData::MINUTE_FIELD) {
+ granularity = DatetimeGranularity::GRANULARITY_MINUTE;
+ }
+ if (data.field_set_mask & DateParseData::SECOND_FIELD) {
+ granularity = DatetimeGranularity::GRANULARITY_SECOND;
+ }
+ return granularity;
+}
+
+} // namespace
+
+bool DatetimeParser::ExtractDatetime(const CompiledRule& rule,
+ const UniLib::RegexMatcher& matcher,
+ const int64 reference_time_ms_utc,
+ const std::string& reference_timezone,
+ const std::string& reference_locale,
+ int locale_id, DatetimeParseResult* result,
+ CodepointSpan* result_span) const {
+ DateParseData parse;
+ DatetimeExtractor extractor(rule, matcher, locale_id, unilib_,
+ extractor_rules_,
+ type_and_locale_to_extractor_rule_);
+ if (!extractor.Extract(&parse, result_span)) {
+ return false;
+ }
+
+ result->granularity = GetGranularity(parse);
+
+ if (!calendar_lib_.InterpretParseData(
+ parse, reference_time_ms_utc, reference_timezone, reference_locale,
+ result->granularity, &(result->time_ms_utc))) {
+ return false;
+ }
+
+ return true;
+}
+
+} // namespace libtextclassifier2
diff --git a/datetime/parser.h b/datetime/parser.h
new file mode 100644
index 0000000..0666607
--- /dev/null
+++ b/datetime/parser.h
@@ -0,0 +1,117 @@
+/*
+ * Copyright (C) 2017 The Android Open Source Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#ifndef LIBTEXTCLASSIFIER_DATETIME_PARSER_H_
+#define LIBTEXTCLASSIFIER_DATETIME_PARSER_H_
+
+#include <memory>
+#include <string>
+#include <unordered_map>
+#include <unordered_set>
+#include <vector>
+
+#include "datetime/extractor.h"
+#include "model_generated.h"
+#include "types.h"
+#include "util/base/integral_types.h"
+#include "util/calendar/calendar.h"
+#include "util/utf8/unilib.h"
+#include "zlib-utils.h"
+
+namespace libtextclassifier2 {
+
+// Parses datetime expressions in the input and resolves them to actual absolute
+// time.
+class DatetimeParser {
+ public:
+ static std::unique_ptr<DatetimeParser> Instance(
+ const DatetimeModel* model, const UniLib& unilib,
+ ZlibDecompressor* decompressor);
+
+ // Parses the dates in 'input' and fills result. Makes sure that the results
+ // do not overlap.
+ // If 'anchor_start_end' is true the extracted results need to start at the
+ // beginning of 'input' and end at the end of it.
+ bool Parse(const std::string& input, int64 reference_time_ms_utc,
+ const std::string& reference_timezone, const std::string& locales,
+ ModeFlag mode, bool anchor_start_end,
+ std::vector<DatetimeParseResultSpan>* results) const;
+
+ // Same as above but takes UnicodeText.
+ bool Parse(const UnicodeText& input, int64 reference_time_ms_utc,
+ const std::string& reference_timezone, const std::string& locales,
+ ModeFlag mode, bool anchor_start_end,
+ std::vector<DatetimeParseResultSpan>* results) const;
+
+ protected:
+ DatetimeParser(const DatetimeModel* model, const UniLib& unilib,
+ ZlibDecompressor* decompressor);
+
+ // Returns a list of locale ids for given locale spec string (comma-separated
+ // locale names). Assigns the first parsed locale to reference_locale.
+ std::vector<int> ParseAndExpandLocales(const std::string& locales,
+ std::string* reference_locale) const;
+
+ // Helper function that finds datetime spans, only using the rules associated
+ // with the given locales.
+ bool FindSpansUsingLocales(
+ const std::vector<int>& locale_ids, const UnicodeText& input,
+ const int64 reference_time_ms_utc, const std::string& reference_timezone,
+ ModeFlag mode, bool anchor_start_end, const std::string& reference_locale,
+ std::unordered_set<int>* executed_rules,
+ std::vector<DatetimeParseResultSpan>* found_spans) const;
+
+ bool ParseWithRule(const CompiledRule& rule, const UnicodeText& input,
+ int64 reference_time_ms_utc,
+ const std::string& reference_timezone,
+ const std::string& reference_locale, const int locale_id,
+ bool anchor_start_end,
+ std::vector<DatetimeParseResultSpan>* result) const;
+
+ // Converts the current match in 'matcher' into DatetimeParseResult.
+ bool ExtractDatetime(const CompiledRule& rule,
+ const UniLib::RegexMatcher& matcher,
+ int64 reference_time_ms_utc,
+ const std::string& reference_timezone,
+ const std::string& reference_locale, int locale_id,
+ DatetimeParseResult* result,
+ CodepointSpan* result_span) const;
+
+ // Parse and extract information from current match in 'matcher'.
+ bool HandleParseMatch(const CompiledRule& rule,
+ const UniLib::RegexMatcher& matcher,
+ int64 reference_time_ms_utc,
+ const std::string& reference_timezone,
+ const std::string& reference_locale, int locale_id,
+ std::vector<DatetimeParseResultSpan>* result) const;
+
+ private:
+ bool initialized_;
+ const UniLib& unilib_;
+ std::vector<CompiledRule> rules_;
+ std::unordered_map<int, std::vector<int>> locale_to_rules_;
+ std::vector<std::unique_ptr<const UniLib::RegexPattern>> extractor_rules_;
+ std::unordered_map<DatetimeExtractorType, std::unordered_map<int, int>>
+ type_and_locale_to_extractor_rule_;
+ std::unordered_map<std::string, int> locale_string_to_id_;
+ std::vector<int> default_locale_ids_;
+ CalendarLib calendar_lib_;
+ bool use_extractors_for_locating_;
+};
+
+} // namespace libtextclassifier2
+
+#endif // LIBTEXTCLASSIFIER_DATETIME_PARSER_H_
diff --git a/datetime/parser_test.cc b/datetime/parser_test.cc
new file mode 100644
index 0000000..e61ed12
--- /dev/null
+++ b/datetime/parser_test.cc
@@ -0,0 +1,457 @@
+/*
+ * Copyright (C) 2017 The Android Open Source Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <time.h>
+#include <fstream>
+#include <iostream>
+#include <memory>
+#include <string>
+
+#include "gmock/gmock.h"
+#include "gtest/gtest.h"
+
+#include "datetime/parser.h"
+#include "model_generated.h"
+#include "text-classifier.h"
+#include "types-test-util.h"
+
+using testing::ElementsAreArray;
+
+namespace libtextclassifier2 {
+namespace {
+
+std::string GetModelPath() {
+ return LIBTEXTCLASSIFIER_TEST_DATA_DIR;
+}
+
+std::string ReadFile(const std::string& file_name) {
+ std::ifstream file_stream(file_name);
+ return std::string(std::istreambuf_iterator<char>(file_stream), {});
+}
+
+std::string FormatMillis(int64 time_ms_utc) {
+ long time_seconds = time_ms_utc / 1000; // NOLINT
+ // Format time, "ddd yyyy-mm-dd hh:mm:ss zzz"
+ char buffer[512];
+ strftime(buffer, sizeof(buffer), "%a %Y-%m-%d %H:%M:%S %Z",
+ localtime(&time_seconds));
+ return std::string(buffer);
+}
+
+class ParserTest : public testing::Test {
+ public:
+ void SetUp() override {
+ model_buffer_ = ReadFile(GetModelPath() + "test_model.fb");
+ classifier_ = TextClassifier::FromUnownedBuffer(
+ model_buffer_.data(), model_buffer_.size(), &unilib_);
+ TC_CHECK(classifier_);
+ parser_ = classifier_->DatetimeParserForTests();
+ }
+
+ bool HasNoResult(const std::string& text, bool anchor_start_end = false,
+ const std::string& timezone = "Europe/Zurich") {
+ std::vector<DatetimeParseResultSpan> results;
+ if (!parser_->Parse(text, 0, timezone, /*locales=*/"", ModeFlag_ANNOTATION,
+ anchor_start_end, &results)) {
+ TC_LOG(ERROR) << text;
+ TC_CHECK(false);
+ }
+ return results.empty();
+ }
+
+ bool ParsesCorrectly(const std::string& marked_text,
+ const int64 expected_ms_utc,
+ DatetimeGranularity expected_granularity,
+ bool anchor_start_end = false,
+ const std::string& timezone = "Europe/Zurich",
+ const std::string& locales = "en-US") {
+ const UnicodeText marked_text_unicode =
+ UTF8ToUnicodeText(marked_text, /*do_copy=*/false);
+ auto brace_open_it =
+ std::find(marked_text_unicode.begin(), marked_text_unicode.end(), '{');
+ auto brace_end_it =
+ std::find(marked_text_unicode.begin(), marked_text_unicode.end(), '}');
+ TC_CHECK(brace_open_it != marked_text_unicode.end());
+ TC_CHECK(brace_end_it != marked_text_unicode.end());
+
+ std::string text;
+ text +=
+ UnicodeText::UTF8Substring(marked_text_unicode.begin(), brace_open_it);
+ text += UnicodeText::UTF8Substring(std::next(brace_open_it), brace_end_it);
+ text += UnicodeText::UTF8Substring(std::next(brace_end_it),
+ marked_text_unicode.end());
+
+ std::vector<DatetimeParseResultSpan> results;
+
+ if (!parser_->Parse(text, 0, timezone, locales, ModeFlag_ANNOTATION,
+ anchor_start_end, &results)) {
+ TC_LOG(ERROR) << text;
+ TC_CHECK(false);
+ }
+ if (results.empty()) {
+ TC_LOG(ERROR) << "No results.";
+ return false;
+ }
+
+ const int expected_start_index =
+ std::distance(marked_text_unicode.begin(), brace_open_it);
+ // The -1 bellow is to account for the opening bracket character.
+ const int expected_end_index =
+ std::distance(marked_text_unicode.begin(), brace_end_it) - 1;
+
+ std::vector<DatetimeParseResultSpan> filtered_results;
+ for (const DatetimeParseResultSpan& result : results) {
+ if (SpansOverlap(result.span,
+ {expected_start_index, expected_end_index})) {
+ filtered_results.push_back(result);
+ }
+ }
+
+ const std::vector<DatetimeParseResultSpan> expected{
+ {{expected_start_index, expected_end_index},
+ {expected_ms_utc, expected_granularity},
+ /*target_classification_score=*/1.0,
+ /*priority_score=*/0.0}};
+ const bool matches =
+ testing::Matches(ElementsAreArray(expected))(filtered_results);
+ if (!matches) {
+ TC_LOG(ERROR) << "Expected: " << expected[0] << " which corresponds to: "
+ << FormatMillis(expected[0].data.time_ms_utc);
+ for (int i = 0; i < filtered_results.size(); ++i) {
+ TC_LOG(ERROR) << "Actual[" << i << "]: " << filtered_results[i]
+ << " which corresponds to: "
+ << FormatMillis(filtered_results[i].data.time_ms_utc);
+ }
+ }
+ return matches;
+ }
+
+ bool ParsesCorrectlyGerman(const std::string& marked_text,
+ const int64 expected_ms_utc,
+ DatetimeGranularity expected_granularity) {
+ return ParsesCorrectly(marked_text, expected_ms_utc, expected_granularity,
+ /*anchor_start_end=*/false,
+ /*timezone=*/"Europe/Zurich", /*locales=*/"de");
+ }
+
+ protected:
+ std::string model_buffer_;
+ std::unique_ptr<TextClassifier> classifier_;
+ const DatetimeParser* parser_;
+ UniLib unilib_;
+};
+
+// Test with just a few cases to make debugging of general failures easier.
+TEST_F(ParserTest, ParseShort) {
+ EXPECT_TRUE(
+ ParsesCorrectly("{January 1, 1988}", 567990000000, GRANULARITY_DAY));
+ EXPECT_TRUE(ParsesCorrectly("{three days ago}", -262800000, GRANULARITY_DAY));
+}
+
+TEST_F(ParserTest, Parse) {
+ EXPECT_TRUE(
+ ParsesCorrectly("{January 1, 1988}", 567990000000, GRANULARITY_DAY));
+ EXPECT_TRUE(
+ ParsesCorrectly("{january 31 2018}", 1517353200000, GRANULARITY_DAY));
+ EXPECT_TRUE(ParsesCorrectly("lorem {1 january 2018} ipsum", 1514761200000,
+ GRANULARITY_DAY));
+ EXPECT_TRUE(ParsesCorrectly("{09/Mar/2004 22:02:40}", 1078866160000,
+ GRANULARITY_SECOND));
+ EXPECT_TRUE(ParsesCorrectly("{Dec 2, 2010 2:39:58 AM}", 1291253998000,
+ GRANULARITY_SECOND));
+ EXPECT_TRUE(ParsesCorrectly("{Jun 09 2011 15:28:14}", 1307626094000,
+ GRANULARITY_SECOND));
+ EXPECT_TRUE(
+ ParsesCorrectly("{Mar 16 08:12:04}", 6419524000, GRANULARITY_SECOND));
+ EXPECT_TRUE(ParsesCorrectly("{2010-06-26 02:31:29},573", 1277512289000,
+ GRANULARITY_SECOND));
+ EXPECT_TRUE(ParsesCorrectly("{2006/01/22 04:11:05}", 1137899465000,
+ GRANULARITY_SECOND));
+ EXPECT_TRUE(ParsesCorrectly("{11:42:35}", 38555000, GRANULARITY_SECOND));
+ EXPECT_TRUE(ParsesCorrectly("{11:42:35}.173", 38555000, GRANULARITY_SECOND));
+ EXPECT_TRUE(
+ ParsesCorrectly("{23/Apr 11:42:35},173", 9715355000, GRANULARITY_SECOND));
+ EXPECT_TRUE(ParsesCorrectly("{23/Apr/2015 11:42:35}", 1429782155000,
+ GRANULARITY_SECOND));
+ EXPECT_TRUE(ParsesCorrectly("{23-Apr-2015 11:42:35}", 1429782155000,
+ GRANULARITY_SECOND));
+ EXPECT_TRUE(ParsesCorrectly("{23-Apr-2015 11:42:35}.883", 1429782155000,
+ GRANULARITY_SECOND));
+ EXPECT_TRUE(ParsesCorrectly("{23 Apr 2015 11:42:35}", 1429782155000,
+ GRANULARITY_SECOND));
+ EXPECT_TRUE(ParsesCorrectly("{23 Apr 2015 11:42:35}.883", 1429782155000,
+ GRANULARITY_SECOND));
+ EXPECT_TRUE(ParsesCorrectly("{04/23/15 11:42:35}", 1429782155000,
+ GRANULARITY_SECOND));
+ EXPECT_TRUE(ParsesCorrectly("{04/23/2015 11:42:35}", 1429782155000,
+ GRANULARITY_SECOND));
+ EXPECT_TRUE(ParsesCorrectly("{04/23/2015 11:42:35}.883", 1429782155000,
+ GRANULARITY_SECOND));
+ EXPECT_TRUE(ParsesCorrectly("{9/28/2011 2:23:15 PM}", 1317212595000,
+ GRANULARITY_SECOND));
+ EXPECT_TRUE(ParsesCorrectly(
+ "Are sentiments apartments decisively the especially alteration. "
+ "Thrown shy denote ten ladies though ask saw. Or by to he going "
+ "think order event music. Incommode so intention defective at "
+ "convinced. Led income months itself and houses you. After nor "
+ "you leave might share court balls. {19/apr/2010 06:36:15} Are "
+ "sentiments apartments decisively the especially alteration. "
+ "Thrown shy denote ten ladies though ask saw. Or by to he going "
+ "think order event music. Incommode so intention defective at "
+ "convinced. Led income months itself and houses you. After nor "
+ "you leave might share court balls. ",
+ 1271651775000, GRANULARITY_SECOND));
+ EXPECT_TRUE(ParsesCorrectly("{january 1 2018 at 4:30}", 1514777400000,
+ GRANULARITY_MINUTE));
+ EXPECT_TRUE(ParsesCorrectly("{january 1 2018 at 4:30 am}", 1514777400000,
+ GRANULARITY_MINUTE));
+ EXPECT_TRUE(ParsesCorrectly("{january 1 2018 at 4pm}", 1514818800000,
+ GRANULARITY_HOUR));
+
+ EXPECT_TRUE(ParsesCorrectly("{today}", -3600000, GRANULARITY_DAY));
+ EXPECT_TRUE(ParsesCorrectly("{today}", -57600000, GRANULARITY_DAY,
+ /*anchor_start_end=*/false,
+ "America/Los_Angeles"));
+ EXPECT_TRUE(ParsesCorrectly("{next week}", 255600000, GRANULARITY_WEEK));
+ EXPECT_TRUE(ParsesCorrectly("{next day}", 82800000, GRANULARITY_DAY));
+ EXPECT_TRUE(ParsesCorrectly("{in three days}", 255600000, GRANULARITY_DAY));
+ EXPECT_TRUE(
+ ParsesCorrectly("{in three weeks}", 1465200000, GRANULARITY_WEEK));
+ EXPECT_TRUE(ParsesCorrectly("{tomorrow}", 82800000, GRANULARITY_DAY));
+ EXPECT_TRUE(
+ ParsesCorrectly("{tomorrow at 4:00}", 97200000, GRANULARITY_MINUTE));
+ EXPECT_TRUE(ParsesCorrectly("{tomorrow at 4}", 97200000, GRANULARITY_HOUR));
+ EXPECT_TRUE(ParsesCorrectly("{next wednesday}", 514800000, GRANULARITY_DAY));
+ EXPECT_TRUE(
+ ParsesCorrectly("{next wednesday at 4}", 529200000, GRANULARITY_HOUR));
+ EXPECT_TRUE(ParsesCorrectly("last seen {today at 9:01 PM}", 72060000,
+ GRANULARITY_MINUTE));
+ EXPECT_TRUE(ParsesCorrectly("{Three days ago}", -262800000, GRANULARITY_DAY));
+ EXPECT_TRUE(ParsesCorrectly("{three days ago}", -262800000, GRANULARITY_DAY));
+}
+
+TEST_F(ParserTest, ParseWithAnchor) {
+ EXPECT_TRUE(ParsesCorrectly("{January 1, 1988}", 567990000000,
+ GRANULARITY_DAY, /*anchor_start_end=*/false));
+ EXPECT_TRUE(ParsesCorrectly("{January 1, 1988}", 567990000000,
+ GRANULARITY_DAY, /*anchor_start_end=*/true));
+ EXPECT_TRUE(ParsesCorrectly("lorem {1 january 2018} ipsum", 1514761200000,
+ GRANULARITY_DAY, /*anchor_start_end=*/false));
+ EXPECT_TRUE(HasNoResult("lorem 1 january 2018 ipsum",
+ /*anchor_start_end=*/true));
+}
+
+TEST_F(ParserTest, ParseGerman) {
+ EXPECT_TRUE(
+ ParsesCorrectlyGerman("{Januar 1 2018}", 1514761200000, GRANULARITY_DAY));
+ EXPECT_TRUE(
+ ParsesCorrectlyGerman("{1 2 2018}", 1517439600000, GRANULARITY_DAY));
+ EXPECT_TRUE(ParsesCorrectlyGerman("lorem {1 Januar 2018} ipsum",
+ 1514761200000, GRANULARITY_DAY));
+ EXPECT_TRUE(ParsesCorrectlyGerman("{19/Apr/2010:06:36:15}", 1271651775000,
+ GRANULARITY_SECOND));
+ EXPECT_TRUE(ParsesCorrectlyGerman("{09/März/2004 22:02:40}", 1078866160000,
+ GRANULARITY_SECOND));
+ EXPECT_TRUE(ParsesCorrectlyGerman("{Dez 2, 2010 2:39:58}", 1291253998000,
+ GRANULARITY_SECOND));
+ EXPECT_TRUE(ParsesCorrectlyGerman("{Juni 09 2011 15:28:14}", 1307626094000,
+ GRANULARITY_SECOND));
+ EXPECT_TRUE(ParsesCorrectlyGerman("{März 16 08:12:04}", 6419524000,
+ GRANULARITY_SECOND));
+ EXPECT_TRUE(ParsesCorrectlyGerman("{2010-06-26 02:31:29},573", 1277512289000,
+ GRANULARITY_SECOND));
+ EXPECT_TRUE(ParsesCorrectlyGerman("{2006/01/22 04:11:05}", 1137899465000,
+ GRANULARITY_SECOND));
+ EXPECT_TRUE(
+ ParsesCorrectlyGerman("{11:42:35}", 38555000, GRANULARITY_SECOND));
+ EXPECT_TRUE(
+ ParsesCorrectlyGerman("{11:42:35}.173", 38555000, GRANULARITY_SECOND));
+ EXPECT_TRUE(ParsesCorrectlyGerman("{23/Apr 11:42:35},173", 9715355000,
+ GRANULARITY_SECOND));
+ EXPECT_TRUE(ParsesCorrectlyGerman("{23/Apr/2015:11:42:35}", 1429782155000,
+ GRANULARITY_SECOND));
+ EXPECT_TRUE(ParsesCorrectlyGerman("{23/Apr/2015 11:42:35}", 1429782155000,
+ GRANULARITY_SECOND));
+ EXPECT_TRUE(ParsesCorrectlyGerman("{23-Apr-2015 11:42:35}", 1429782155000,
+ GRANULARITY_SECOND));
+ EXPECT_TRUE(ParsesCorrectlyGerman("{23-Apr-2015 11:42:35}.883", 1429782155000,
+ GRANULARITY_SECOND));
+ EXPECT_TRUE(ParsesCorrectlyGerman("{23 Apr 2015 11:42:35}", 1429782155000,
+ GRANULARITY_SECOND));
+ EXPECT_TRUE(ParsesCorrectlyGerman("{23 Apr 2015 11:42:35}.883", 1429782155000,
+ GRANULARITY_SECOND));
+ EXPECT_TRUE(ParsesCorrectlyGerman("{04/23/15 11:42:35}", 1429782155000,
+ GRANULARITY_SECOND));
+ EXPECT_TRUE(ParsesCorrectlyGerman("{04/23/2015 11:42:35}", 1429782155000,
+ GRANULARITY_SECOND));
+ EXPECT_TRUE(ParsesCorrectlyGerman("{04/23/2015 11:42:35}.883", 1429782155000,
+ GRANULARITY_SECOND));
+ EXPECT_TRUE(ParsesCorrectlyGerman("{19/apr/2010:06:36:15}", 1271651775000,
+ GRANULARITY_SECOND));
+ EXPECT_TRUE(ParsesCorrectlyGerman("{januar 1 2018 um 4:30}", 1514777400000,
+ GRANULARITY_MINUTE));
+ EXPECT_TRUE(ParsesCorrectlyGerman("{januar 1 2018 um 4:30 nachm}",
+ 1514820600000, GRANULARITY_MINUTE));
+ EXPECT_TRUE(ParsesCorrectlyGerman("{januar 1 2018 um 4 nachm}", 1514818800000,
+ GRANULARITY_HOUR));
+ EXPECT_TRUE(
+ ParsesCorrectlyGerman("{14.03.2017}", 1489446000000, GRANULARITY_DAY));
+ EXPECT_TRUE(ParsesCorrectlyGerman("{heute}", -3600000, GRANULARITY_DAY));
+ EXPECT_TRUE(
+ ParsesCorrectlyGerman("{nächste Woche}", 342000000, GRANULARITY_WEEK));
+ EXPECT_TRUE(
+ ParsesCorrectlyGerman("{nächsten Tag}", 82800000, GRANULARITY_DAY));
+ EXPECT_TRUE(
+ ParsesCorrectlyGerman("{in drei Tagen}", 255600000, GRANULARITY_DAY));
+ EXPECT_TRUE(
+ ParsesCorrectlyGerman("{in drei Wochen}", 1551600000, GRANULARITY_WEEK));
+ EXPECT_TRUE(
+ ParsesCorrectlyGerman("{vor drei Tagen}", -262800000, GRANULARITY_DAY));
+ EXPECT_TRUE(ParsesCorrectlyGerman("{morgen}", 82800000, GRANULARITY_DAY));
+ EXPECT_TRUE(
+ ParsesCorrectlyGerman("{morgen um 4:00}", 97200000, GRANULARITY_MINUTE));
+ EXPECT_TRUE(
+ ParsesCorrectlyGerman("{morgen um 4}", 97200000, GRANULARITY_HOUR));
+ EXPECT_TRUE(
+ ParsesCorrectlyGerman("{nächsten Mittwoch}", 514800000, GRANULARITY_DAY));
+ EXPECT_TRUE(ParsesCorrectlyGerman("{nächsten Mittwoch um 4}", 529200000,
+ GRANULARITY_HOUR));
+ EXPECT_TRUE(
+ ParsesCorrectlyGerman("{Vor drei Tagen}", -262800000, GRANULARITY_DAY));
+ EXPECT_TRUE(
+ ParsesCorrectlyGerman("{in einer woche}", 342000000, GRANULARITY_WEEK));
+ EXPECT_TRUE(
+ ParsesCorrectlyGerman("{in einer tag}", 82800000, GRANULARITY_DAY));
+}
+
+TEST_F(ParserTest, ParseNonUs) {
+ EXPECT_TRUE(ParsesCorrectly("{1/5/15}", 1430431200000, GRANULARITY_DAY,
+ /*anchor_start_end=*/false,
+ /*timezone=*/"Europe/Zurich",
+ /*locales=*/"en-GB"));
+ EXPECT_TRUE(ParsesCorrectly("{1/5/15}", 1430431200000, GRANULARITY_DAY,
+ /*anchor_start_end=*/false,
+ /*timezone=*/"Europe/Zurich", /*locales=*/"en"));
+}
+
+TEST_F(ParserTest, ParseUs) {
+ EXPECT_TRUE(ParsesCorrectly("{1/5/15}", 1420412400000, GRANULARITY_DAY,
+ /*anchor_start_end=*/false,
+ /*timezone=*/"Europe/Zurich",
+ /*locales=*/"en-US"));
+ EXPECT_TRUE(ParsesCorrectly("{1/5/15}", 1420412400000, GRANULARITY_DAY,
+ /*anchor_start_end=*/false,
+ /*timezone=*/"Europe/Zurich",
+ /*locales=*/"es-US"));
+}
+
+TEST_F(ParserTest, ParseUnknownLanguage) {
+ EXPECT_TRUE(ParsesCorrectly("bylo to {31. 12. 2015} v 6 hodin", 1451516400000,
+ GRANULARITY_DAY,
+ /*anchor_start_end=*/false,
+ /*timezone=*/"Europe/Zurich", /*locales=*/"xx"));
+}
+
+class ParserLocaleTest : public testing::Test {
+ public:
+ void SetUp() override;
+ bool HasResult(const std::string& input, const std::string& locales);
+
+ protected:
+ UniLib unilib_;
+ flatbuffers::FlatBufferBuilder builder_;
+ std::unique_ptr<DatetimeParser> parser_;
+};
+
+void AddPattern(const std::string& regex, int locale,
+ std::vector<std::unique_ptr<DatetimeModelPatternT>>* patterns) {
+ patterns->emplace_back(new DatetimeModelPatternT);
+ patterns->back()->regexes.emplace_back(new DatetimeModelPattern_::RegexT);
+ patterns->back()->regexes.back()->pattern = regex;
+ patterns->back()->regexes.back()->groups.push_back(
+ DatetimeGroupType_GROUP_UNUSED);
+ patterns->back()->locales.push_back(locale);
+}
+
+void ParserLocaleTest::SetUp() {
+ DatetimeModelT model;
+ model.use_extractors_for_locating = false;
+ model.locales.clear();
+ model.locales.push_back("en-US");
+ model.locales.push_back("en-CH");
+ model.locales.push_back("zh-Hant");
+ model.locales.push_back("en-*");
+ model.locales.push_back("zh-Hant-*");
+ model.locales.push_back("*-CH");
+ model.locales.push_back("default");
+ model.default_locales.push_back(6);
+
+ AddPattern(/*regex=*/"en-US", /*locale=*/0, &model.patterns);
+ AddPattern(/*regex=*/"en-CH", /*locale=*/1, &model.patterns);
+ AddPattern(/*regex=*/"zh-Hant", /*locale=*/2, &model.patterns);
+ AddPattern(/*regex=*/"en-all", /*locale=*/3, &model.patterns);
+ AddPattern(/*regex=*/"zh-Hant-all", /*locale=*/4, &model.patterns);
+ AddPattern(/*regex=*/"all-CH", /*locale=*/5, &model.patterns);
+ AddPattern(/*regex=*/"default", /*locale=*/6, &model.patterns);
+
+ builder_.Finish(DatetimeModel::Pack(builder_, &model));
+ const DatetimeModel* model_fb =
+ flatbuffers::GetRoot<DatetimeModel>(builder_.GetBufferPointer());
+ ASSERT_TRUE(model_fb);
+
+ parser_ = DatetimeParser::Instance(model_fb, unilib_,
+ /*decompressor=*/nullptr);
+ ASSERT_TRUE(parser_);
+}
+
+bool ParserLocaleTest::HasResult(const std::string& input,
+ const std::string& locales) {
+ std::vector<DatetimeParseResultSpan> results;
+ EXPECT_TRUE(parser_->Parse(input, /*reference_time_ms_utc=*/0,
+ /*reference_timezone=*/"", locales,
+ ModeFlag_ANNOTATION, false, &results));
+ return results.size() == 1;
+}
+
+TEST_F(ParserLocaleTest, English) {
+ EXPECT_TRUE(HasResult("en-US", /*locales=*/"en-US"));
+ EXPECT_FALSE(HasResult("en-CH", /*locales=*/"en-US"));
+ EXPECT_FALSE(HasResult("en-US", /*locales=*/"en-CH"));
+ EXPECT_TRUE(HasResult("en-CH", /*locales=*/"en-CH"));
+ EXPECT_TRUE(HasResult("default", /*locales=*/"en-CH"));
+}
+
+TEST_F(ParserLocaleTest, TraditionalChinese) {
+ EXPECT_TRUE(HasResult("zh-Hant-all", /*locales=*/"zh-Hant"));
+ EXPECT_TRUE(HasResult("zh-Hant-all", /*locales=*/"zh-Hant-TW"));
+ EXPECT_TRUE(HasResult("zh-Hant-all", /*locales=*/"zh-Hant-SG"));
+ EXPECT_FALSE(HasResult("zh-Hant-all", /*locales=*/"zh-SG"));
+ EXPECT_FALSE(HasResult("zh-Hant-all", /*locales=*/"zh"));
+ EXPECT_TRUE(HasResult("default", /*locales=*/"zh"));
+ EXPECT_TRUE(HasResult("default", /*locales=*/"zh-Hant-SG"));
+}
+
+TEST_F(ParserLocaleTest, SwissEnglish) {
+ EXPECT_TRUE(HasResult("all-CH", /*locales=*/"de-CH"));
+ EXPECT_TRUE(HasResult("all-CH", /*locales=*/"en-CH"));
+ EXPECT_TRUE(HasResult("en-all", /*locales=*/"en-CH"));
+ EXPECT_FALSE(HasResult("all-CH", /*locales=*/"de-DE"));
+ EXPECT_TRUE(HasResult("default", /*locales=*/"de-CH"));
+ EXPECT_TRUE(HasResult("default", /*locales=*/"en-CH"));
+}
+
+} // namespace
+} // namespace libtextclassifier2
diff --git a/smartselect/feature-processor.cc b/feature-processor.cc
similarity index 65%
rename from smartselect/feature-processor.cc
rename to feature-processor.cc
index c1db95a..551e649 100644
--- a/smartselect/feature-processor.cc
+++ b/feature-processor.cc
@@ -14,59 +14,51 @@
* limitations under the License.
*/
-#include "smartselect/feature-processor.h"
+#include "feature-processor.h"
#include <iterator>
#include <set>
#include <vector>
-#include "smartselect/text-classification-model.pb.h"
#include "util/base/logging.h"
#include "util/strings/utf8.h"
#include "util/utf8/unicodetext.h"
-#ifndef LIBTEXTCLASSIFIER_DISABLE_ICU_SUPPORT
-#include "unicode/brkiter.h"
-#include "unicode/errorcode.h"
-#include "unicode/uchar.h"
-#endif
-namespace libtextclassifier {
+namespace libtextclassifier2 {
namespace internal {
TokenFeatureExtractorOptions BuildTokenFeatureExtractorOptions(
- const FeatureProcessorOptions& options) {
+ const FeatureProcessorOptions* const options) {
TokenFeatureExtractorOptions extractor_options;
- extractor_options.num_buckets = options.num_buckets();
- for (int order : options.chargram_orders()) {
- extractor_options.chargram_orders.push_back(order);
+ extractor_options.num_buckets = options->num_buckets();
+ if (options->chargram_orders() != nullptr) {
+ for (int order : *options->chargram_orders()) {
+ extractor_options.chargram_orders.push_back(order);
+ }
}
- extractor_options.max_word_length = options.max_word_length();
- extractor_options.extract_case_feature = options.extract_case_feature();
- extractor_options.unicode_aware_features = options.unicode_aware_features();
+ extractor_options.max_word_length = options->max_word_length();
+ extractor_options.extract_case_feature = options->extract_case_feature();
+ extractor_options.unicode_aware_features = options->unicode_aware_features();
extractor_options.extract_selection_mask_feature =
- options.extract_selection_mask_feature();
- for (int i = 0; i < options.regexp_feature_size(); ++i) {
- extractor_options.regexp_features.push_back(options.regexp_feature(i));
+ options->extract_selection_mask_feature();
+ if (options->regexp_feature() != nullptr) {
+ for (const auto& regexp_feauture : *options->regexp_feature()) {
+ extractor_options.regexp_features.push_back(regexp_feauture->str());
+ }
}
- extractor_options.remap_digits = options.remap_digits();
- extractor_options.lowercase_tokens = options.lowercase_tokens();
+ extractor_options.remap_digits = options->remap_digits();
+ extractor_options.lowercase_tokens = options->lowercase_tokens();
- for (const auto& chargram : options.allowed_chargrams()) {
- extractor_options.allowed_chargrams.insert(chargram);
+ if (options->allowed_chargrams() != nullptr) {
+ for (const auto& chargram : *options->allowed_chargrams()) {
+ extractor_options.allowed_chargrams.insert(chargram->str());
+ }
}
-
return extractor_options;
}
-FeatureProcessorOptions ParseSerializedOptions(
- const std::string& serialized_options) {
- FeatureProcessorOptions options;
- options.ParseFromString(serialized_options);
- return options;
-}
-
void SplitTokensOnSelectionBoundaries(CodepointSpan selection,
std::vector<Token>* tokens) {
for (auto it = tokens->begin(); it != tokens->end(); ++it) {
@@ -119,6 +111,16 @@
}
}
+const UniLib* MaybeCreateUnilib(const UniLib* unilib,
+ std::unique_ptr<UniLib>* owned_unilib) {
+ if (unilib) {
+ return unilib;
+ } else {
+ owned_unilib->reset(new UniLib);
+ return owned_unilib->get();
+ }
+}
+
} // namespace internal
void FeatureProcessor::StripTokensFromOtherLines(
@@ -126,6 +128,12 @@
std::vector<Token>* tokens) const {
const UnicodeText context_unicode = UTF8ToUnicodeText(context,
/*do_copy=*/false);
+ StripTokensFromOtherLines(context_unicode, span, tokens);
+}
+
+void FeatureProcessor::StripTokensFromOtherLines(
+ const UnicodeText& context_unicode, CodepointSpan span,
+ std::vector<Token>* tokens) const {
std::vector<UnicodeTextRange> lines = SplitContext(context_unicode);
auto span_start = context_unicode.begin();
@@ -157,37 +165,43 @@
}
std::string FeatureProcessor::GetDefaultCollection() const {
- if (options_.default_collection() < 0 ||
- options_.default_collection() >= options_.collections_size()) {
+ if (options_->default_collection() < 0 ||
+ options_->collections() == nullptr ||
+ options_->default_collection() >= options_->collections()->size()) {
TC_LOG(ERROR)
<< "Invalid or missing default collection. Returning empty string.";
return "";
}
- return options_.collections(options_.default_collection());
+ return (*options_->collections())[options_->default_collection()]->str();
+}
+
+std::vector<Token> FeatureProcessor::Tokenize(const std::string& text) const {
+ const UnicodeText text_unicode = UTF8ToUnicodeText(text, /*do_copy=*/false);
+ return Tokenize(text_unicode);
}
std::vector<Token> FeatureProcessor::Tokenize(
- const std::string& utf8_text) const {
- if (options_.tokenization_type() ==
- libtextclassifier::FeatureProcessorOptions::INTERNAL_TOKENIZER) {
- return tokenizer_.Tokenize(utf8_text);
- } else if (options_.tokenization_type() ==
- libtextclassifier::FeatureProcessorOptions::ICU ||
- options_.tokenization_type() ==
- libtextclassifier::FeatureProcessorOptions::MIXED) {
+ const UnicodeText& text_unicode) const {
+ if (options_->tokenization_type() ==
+ FeatureProcessorOptions_::TokenizationType_INTERNAL_TOKENIZER) {
+ return tokenizer_.Tokenize(text_unicode);
+ } else if (options_->tokenization_type() ==
+ FeatureProcessorOptions_::TokenizationType_ICU ||
+ options_->tokenization_type() ==
+ FeatureProcessorOptions_::TokenizationType_MIXED) {
std::vector<Token> result;
- if (!ICUTokenize(utf8_text, &result)) {
+ if (!ICUTokenize(text_unicode, &result)) {
return {};
}
- if (options_.tokenization_type() ==
- libtextclassifier::FeatureProcessorOptions::MIXED) {
- InternalRetokenize(utf8_text, &result);
+ if (options_->tokenization_type() ==
+ FeatureProcessorOptions_::TokenizationType_MIXED) {
+ InternalRetokenize(text_unicode, &result);
}
return result;
} else {
TC_LOG(ERROR) << "Unknown tokenization type specified. Using "
"internal.";
- return tokenizer_.Tokenize(utf8_text);
+ return tokenizer_.Tokenize(text_unicode);
}
}
@@ -205,11 +219,11 @@
const int result_begin_token_index = token_span.first;
const Token& result_begin_token =
- tokens[options_.context_size() - result_begin_token_index];
+ tokens[options_->context_size() - result_begin_token_index];
const int result_begin_codepoint = result_begin_token.start;
const int result_end_token_index = token_span.second;
const Token& result_end_token =
- tokens[options_.context_size() + result_end_token_index];
+ tokens[options_->context_size() + result_end_token_index];
const int result_end_codepoint = result_end_token.end;
if (result_begin_codepoint == kInvalidIndex ||
@@ -224,9 +238,11 @@
UnicodeText::const_iterator token_end = token_end_unicode.end();
const int begin_ignored = CountIgnoredSpanBoundaryCodepoints(
- token_begin, token_begin_unicode.end(), /*count_from_beginning=*/true);
- const int end_ignored = CountIgnoredSpanBoundaryCodepoints(
- token_end_unicode.begin(), token_end, /*count_from_beginning=*/false);
+ token_begin, token_begin_unicode.end(),
+ /*count_from_beginning=*/true);
+ const int end_ignored =
+ CountIgnoredSpanBoundaryCodepoints(token_end_unicode.begin(), token_end,
+ /*count_from_beginning=*/false);
// In case everything would be stripped, set the span to the original
// beginning and zero length.
if (begin_ignored == (result_end_codepoint - result_begin_codepoint)) {
@@ -257,8 +273,8 @@
}
const int click_position =
- options_.context_size(); // Click is always in the middle.
- const int padding = options_.context_size() - options_.max_selection_span();
+ options_->context_size(); // Click is always in the middle.
+ const int padding = options_->context_size() - options_->max_selection_span();
int span_left = 0;
for (int i = click_position - 1; i >= padding; i--) {
@@ -282,7 +298,7 @@
bool tokens_match_span;
const CodepointIndex tokens_start = tokens[click_position - span_left].start;
const CodepointIndex tokens_end = tokens[click_position + span_right].end;
- if (options_.snap_label_span_boundaries_to_containing_tokens()) {
+ if (options_->snap_label_span_boundaries_to_containing_tokens()) {
tokens_match_span = tokens_start <= span.first && tokens_end >= span.second;
} else {
const UnicodeText token_left_unicode = UTF8ToUnicodeText(
@@ -296,7 +312,8 @@
const int num_punctuation_start = CountIgnoredSpanBoundaryCodepoints(
span_begin, token_left_unicode.end(), /*count_from_beginning=*/true);
const int num_punctuation_end = CountIgnoredSpanBoundaryCodepoints(
- token_right_unicode.begin(), span_end, /*count_from_beginning=*/false);
+ token_right_unicode.begin(), span_end,
+ /*count_from_beginning=*/false);
tokens_match_span = tokens_start <= span.first &&
tokens_start + num_punctuation_start >= span.first &&
@@ -422,19 +439,22 @@
int FeatureProcessor::FindCenterToken(CodepointSpan span,
const std::vector<Token>& tokens) const {
- if (options_.center_token_selection_method() ==
- FeatureProcessorOptions::CENTER_TOKEN_FROM_CLICK) {
+ if (options_->center_token_selection_method() ==
+ FeatureProcessorOptions_::
+ CenterTokenSelectionMethod_CENTER_TOKEN_FROM_CLICK) {
return internal::CenterTokenFromClick(span, tokens);
- } else if (options_.center_token_selection_method() ==
- FeatureProcessorOptions::CENTER_TOKEN_MIDDLE_OF_SELECTION) {
+ } else if (options_->center_token_selection_method() ==
+ FeatureProcessorOptions_::
+ CenterTokenSelectionMethod_CENTER_TOKEN_MIDDLE_OF_SELECTION) {
return internal::CenterTokenFromMiddleOfSelection(span, tokens);
- } else if (options_.center_token_selection_method() ==
- FeatureProcessorOptions::DEFAULT_CENTER_TOKEN_METHOD) {
+ } else if (options_->center_token_selection_method() ==
+ FeatureProcessorOptions_::
+ CenterTokenSelectionMethod_DEFAULT_CENTER_TOKEN_METHOD) {
// TODO(zilka): Remove once we have new models on the device.
// It uses the fact that sharing model use
// split_tokens_on_selection_boundaries and selection not. So depending on
// this we select the right way of finding the click location.
- if (!options_.split_tokens_on_selection_boundaries()) {
+ if (!options_->split_tokens_on_selection_boundaries()) {
// SmartSelection model.
return internal::CenterTokenFromClick(span, tokens);
} else {
@@ -462,15 +482,15 @@
}
void FeatureProcessor::PrepareCodepointRanges(
- const std::vector<FeatureProcessorOptions::CodepointRange>&
+ const std::vector<const FeatureProcessorOptions_::CodepointRange*>&
codepoint_ranges,
std::vector<CodepointRange>* prepared_codepoint_ranges) {
prepared_codepoint_ranges->clear();
prepared_codepoint_ranges->reserve(codepoint_ranges.size());
- for (const FeatureProcessorOptions::CodepointRange& range :
+ for (const FeatureProcessorOptions_::CodepointRange* range :
codepoint_ranges) {
prepared_codepoint_ranges->push_back(
- CodepointRange(range.start(), range.end()));
+ CodepointRange(range->start(), range->end()));
}
std::sort(prepared_codepoint_ranges->begin(),
@@ -481,8 +501,10 @@
}
void FeatureProcessor::PrepareIgnoredSpanBoundaryCodepoints() {
- for (const int codepoint : options_.ignored_span_boundary_codepoints()) {
- ignored_span_boundary_codepoints_.insert(codepoint);
+ if (options_->ignored_span_boundary_codepoints() != nullptr) {
+ for (const int codepoint : *options_->ignored_span_boundary_codepoints()) {
+ ignored_span_boundary_codepoints_.insert(codepoint);
+ }
}
}
@@ -555,22 +577,25 @@
std::vector<UnicodeTextRange> FeatureProcessor::SplitContext(
const UnicodeText& context_unicode) const {
- if (options_.only_use_line_with_click()) {
- std::vector<UnicodeTextRange> lines;
- std::set<char32> codepoints;
- codepoints.insert('\n');
- codepoints.insert('|');
- FindSubstrings(context_unicode, codepoints, &lines);
- return lines;
- } else {
- return {{context_unicode.begin(), context_unicode.end()}};
- }
+ std::vector<UnicodeTextRange> lines;
+ const std::set<char32> codepoints{{'\n', '|'}};
+ FindSubstrings(context_unicode, codepoints, &lines);
+ return lines;
}
CodepointSpan FeatureProcessor::StripBoundaryCodepoints(
const std::string& context, CodepointSpan span) const {
const UnicodeText context_unicode =
UTF8ToUnicodeText(context, /*do_copy=*/false);
+ return StripBoundaryCodepoints(context_unicode, span);
+}
+
+CodepointSpan FeatureProcessor::StripBoundaryCodepoints(
+ const UnicodeText& context_unicode, CodepointSpan span) const {
+ if (context_unicode.empty() || !ValidNonEmptySpan(span)) {
+ return span;
+ }
+
UnicodeText::const_iterator span_begin = context_unicode.begin();
std::advance(span_begin, span.first);
UnicodeText::const_iterator span_end = context_unicode.begin();
@@ -589,21 +614,17 @@
}
float FeatureProcessor::SupportedCodepointsRatio(
- int click_pos, const std::vector<Token>& tokens) const {
+ const TokenSpan& token_span, const std::vector<Token>& tokens) const {
int num_supported = 0;
int num_total = 0;
- for (int i = click_pos - options_.context_size();
- i <= click_pos + options_.context_size(); ++i) {
- const bool is_valid_token = i >= 0 && i < tokens.size();
- if (is_valid_token) {
- const UnicodeText value =
- UTF8ToUnicodeText(tokens[i].value, /*do_copy=*/false);
- for (auto codepoint : value) {
- if (IsCodepointInRanges(codepoint, supported_codepoint_ranges_)) {
- ++num_supported;
- }
- ++num_total;
+ for (int i = token_span.first; i < token_span.second; ++i) {
+ const UnicodeText value =
+ UTF8ToUnicodeText(tokens[i].value, /*do_copy=*/false);
+ for (auto codepoint : value) {
+ if (IsCodepointInRanges(codepoint, supported_codepoint_ranges_)) {
+ ++num_supported;
}
+ ++num_total;
}
}
return static_cast<float>(num_supported) / static_cast<float>(num_total);
@@ -640,7 +661,7 @@
int FeatureProcessor::CollectionToLabel(const std::string& collection) const {
const auto it = collection_to_label_.find(collection);
if (it == collection_to_label_.end()) {
- return options_.default_collection();
+ return options_->default_collection();
} else {
return it->second;
}
@@ -648,22 +669,24 @@
std::string FeatureProcessor::LabelToCollection(int label) const {
if (label >= 0 && label < collection_to_label_.size()) {
- return options_.collections(label);
+ return (*options_->collections())[label]->str();
} else {
return GetDefaultCollection();
}
}
void FeatureProcessor::MakeLabelMaps() {
- for (int i = 0; i < options_.collections().size(); ++i) {
- collection_to_label_[options_.collections(i)] = i;
+ if (options_->collections() != nullptr) {
+ for (int i = 0; i < options_->collections()->size(); ++i) {
+ collection_to_label_[(*options_->collections())[i]->str()] = i;
+ }
}
int selection_label_id = 0;
- for (int l = 0; l < (options_.max_selection_span() + 1); ++l) {
- for (int r = 0; r < (options_.max_selection_span() + 1); ++r) {
- if (!options_.selection_reduced_output_space() ||
- r + l <= options_.max_selection_span()) {
+ for (int l = 0; l < (options_->max_selection_span() + 1); ++l) {
+ for (int r = 0; r < (options_->max_selection_span() + 1); ++r) {
+ if (!options_->selection_reduced_output_space() ||
+ r + l <= options_->max_selection_span()) {
TokenSpan token_span{l, r};
selection_to_label_[token_span] = selection_label_id;
label_to_selection_.push_back(token_span);
@@ -673,19 +696,29 @@
}
}
-void FeatureProcessor::TokenizeAndFindClick(const std::string& context,
- CodepointSpan input_span,
- std::vector<Token>* tokens,
- int* click_pos) const {
- TC_CHECK(tokens != nullptr);
- *tokens = Tokenize(context);
+void FeatureProcessor::RetokenizeAndFindClick(const std::string& context,
+ CodepointSpan input_span,
+ bool only_use_line_with_click,
+ std::vector<Token>* tokens,
+ int* click_pos) const {
+ const UnicodeText context_unicode =
+ UTF8ToUnicodeText(context, /*do_copy=*/false);
+ RetokenizeAndFindClick(context_unicode, input_span, only_use_line_with_click,
+ tokens, click_pos);
+}
- if (options_.split_tokens_on_selection_boundaries()) {
+void FeatureProcessor::RetokenizeAndFindClick(
+ const UnicodeText& context_unicode, CodepointSpan input_span,
+ bool only_use_line_with_click, std::vector<Token>* tokens,
+ int* click_pos) const {
+ TC_CHECK(tokens != nullptr);
+
+ if (options_->split_tokens_on_selection_boundaries()) {
internal::SplitTokensOnSelectionBoundaries(input_span, tokens);
}
- if (options_.only_use_line_with_click()) {
- StripTokensFromOtherLines(context, input_span, tokens);
+ if (only_use_line_with_click) {
+ StripTokensFromOtherLines(context_unicode, input_span, tokens);
}
int local_click_pos;
@@ -693,6 +726,11 @@
click_pos = &local_click_pos;
}
*click_pos = FindCenterToken(input_span, *tokens);
+ if (*click_pos == kInvalidIndex) {
+ // If the default click method failed, let's try to do sub-token matching
+ // before we fail.
+ *click_pos = internal::CenterTokenFromClick(input_span, *tokens);
+ }
}
namespace internal {
@@ -733,126 +771,104 @@
} // namespace internal
-bool FeatureProcessor::ExtractFeatures(
- const std::string& context, CodepointSpan input_span,
- TokenSpan relative_click_span, const FeatureVectorFn& feature_vector_fn,
- int feature_vector_size, std::vector<Token>* tokens, int* click_pos,
- std::unique_ptr<CachedFeatures>* cached_features) const {
- TokenizeAndFindClick(context, input_span, tokens, click_pos);
-
- if (input_span.first != kInvalidIndex && input_span.second != kInvalidIndex) {
- // If the default click method failed, let's try to do sub-token matching
- // before we fail.
- if (*click_pos == kInvalidIndex) {
- *click_pos = internal::CenterTokenFromClick(input_span, *tokens);
- if (*click_pos == kInvalidIndex) {
- return false;
- }
- }
- } else {
- // If input_span is unspecified, click the first token and extract features
- // from all tokens.
- *click_pos = 0;
- relative_click_span = {0, tokens->size()};
- }
-
- internal::StripOrPadTokens(relative_click_span, options_.context_size(),
- tokens, click_pos);
-
- if (options_.min_supported_codepoint_ratio() > 0) {
+bool FeatureProcessor::HasEnoughSupportedCodepoints(
+ const std::vector<Token>& tokens, TokenSpan token_span) const {
+ if (options_->min_supported_codepoint_ratio() > 0) {
const float supported_codepoint_ratio =
- SupportedCodepointsRatio(*click_pos, *tokens);
- if (supported_codepoint_ratio < options_.min_supported_codepoint_ratio()) {
+ SupportedCodepointsRatio(token_span, tokens);
+ if (supported_codepoint_ratio < options_->min_supported_codepoint_ratio()) {
TC_VLOG(1) << "Not enough supported codepoints in the context: "
<< supported_codepoint_ratio;
return false;
}
}
+ return true;
+}
- std::vector<std::vector<int>> sparse_features(tokens->size());
- std::vector<std::vector<float>> dense_features(tokens->size());
- for (int i = 0; i < tokens->size(); ++i) {
- const Token& token = (*tokens)[i];
- if (!feature_extractor_.Extract(token, token.IsContainedInSpan(input_span),
- &(sparse_features[i]),
- &(dense_features[i]))) {
- TC_LOG(ERROR) << "Could not extract token's features: " << token;
+bool FeatureProcessor::ExtractFeatures(
+ const std::vector<Token>& tokens, TokenSpan token_span,
+ CodepointSpan selection_span_for_feature,
+ const EmbeddingExecutor* embedding_executor,
+ EmbeddingCache* embedding_cache, int feature_vector_size,
+ std::unique_ptr<CachedFeatures>* cached_features) const {
+ std::unique_ptr<std::vector<float>> features(new std::vector<float>());
+ features->reserve(feature_vector_size * TokenSpanSize(token_span));
+ for (int i = token_span.first; i < token_span.second; ++i) {
+ if (!AppendTokenFeaturesWithCache(tokens[i], selection_span_for_feature,
+ embedding_executor, embedding_cache,
+ features.get())) {
+ TC_LOG(ERROR) << "Could not get token features.";
return false;
}
}
- cached_features->reset(new CachedFeatures(
- *tokens, options_.context_size(), sparse_features, dense_features,
- feature_vector_fn, feature_vector_size));
-
- if (*cached_features == nullptr) {
+ std::unique_ptr<std::vector<float>> padding_features(
+ new std::vector<float>());
+ padding_features->reserve(feature_vector_size);
+ if (!AppendTokenFeaturesWithCache(Token(), selection_span_for_feature,
+ embedding_executor, embedding_cache,
+ padding_features.get())) {
+ TC_LOG(ERROR) << "Count not get padding token features.";
return false;
}
- if (options_.feature_version() == 0) {
- (*cached_features)
- ->SetV0FeatureMode(feature_vector_size -
- feature_extractor_.DenseFeaturesCount());
+ *cached_features = CachedFeatures::Create(token_span, std::move(features),
+ std::move(padding_features),
+ options_, feature_vector_size);
+ if (!*cached_features) {
+ TC_LOG(ERROR) << "Cound not create cached features.";
+ return false;
}
return true;
}
-bool FeatureProcessor::ICUTokenize(const std::string& context,
+bool FeatureProcessor::ICUTokenize(const UnicodeText& context_unicode,
std::vector<Token>* result) const {
-#ifndef LIBTEXTCLASSIFIER_DISABLE_ICU_SUPPORT
- icu::ErrorCode status;
- icu::UnicodeString unicode_text = icu::UnicodeString::fromUTF8(context);
- std::unique_ptr<icu::BreakIterator> break_iterator(
- icu::BreakIterator::createWordInstance(icu::Locale("en"), status));
- if (!status.isSuccess()) {
- TC_LOG(ERROR) << "Break iterator did not initialize properly: "
- << status.errorName();
+ std::unique_ptr<UniLib::BreakIterator> break_iterator =
+ unilib_->CreateBreakIterator(context_unicode);
+ if (!break_iterator) {
return false;
}
-
- break_iterator->setText(unicode_text);
-
- size_t last_break_index = 0;
- size_t break_index = 0;
- size_t last_unicode_index = 0;
- size_t unicode_index = 0;
- while ((break_index = break_iterator->next()) != icu::BreakIterator::DONE) {
- icu::UnicodeString token(unicode_text, last_break_index,
- break_index - last_break_index);
- int token_length = token.countChar32();
+ int last_break_index = 0;
+ int break_index = 0;
+ int last_unicode_index = 0;
+ int unicode_index = 0;
+ auto token_begin_it = context_unicode.begin();
+ while ((break_index = break_iterator->Next()) !=
+ UniLib::BreakIterator::kDone) {
+ const int token_length = break_index - last_break_index;
unicode_index = last_unicode_index + token_length;
- std::string token_utf8;
- token.toUTF8String(token_utf8);
+ auto token_end_it = token_begin_it;
+ std::advance(token_end_it, token_length);
+ // Determine if the whole token is whitespace.
bool is_whitespace = true;
- for (int i = 0; i < token.length(); i++) {
- if (!u_isWhitespace(token.char32At(i))) {
+ for (auto char_it = token_begin_it; char_it < token_end_it; ++char_it) {
+ if (!unilib_->IsWhitespace(*char_it)) {
is_whitespace = false;
+ break;
}
}
- if (!is_whitespace || options_.icu_preserve_whitespace_tokens()) {
- result->push_back(Token(token_utf8, last_unicode_index, unicode_index));
+ const std::string token =
+ context_unicode.UTF8Substring(token_begin_it, token_end_it);
+
+ if (!is_whitespace || options_->icu_preserve_whitespace_tokens()) {
+ result->push_back(Token(token, last_unicode_index, unicode_index));
}
last_break_index = break_index;
last_unicode_index = unicode_index;
+ token_begin_it = token_end_it;
}
return true;
-#else
- TC_LOG(WARNING) << "Can't tokenize, ICU not supported";
- return false;
-#endif
}
-void FeatureProcessor::InternalRetokenize(const std::string& context,
+void FeatureProcessor::InternalRetokenize(const UnicodeText& unicode_text,
std::vector<Token>* tokens) const {
- const UnicodeText unicode_text =
- UTF8ToUnicodeText(context, /*do_copy=*/false);
-
std::vector<Token> result;
CodepointSpan span(-1, -1);
for (Token& token : *tokens) {
@@ -914,4 +930,69 @@
}
}
-} // namespace libtextclassifier
+bool FeatureProcessor::AppendTokenFeaturesWithCache(
+ const Token& token, CodepointSpan selection_span_for_feature,
+ const EmbeddingExecutor* embedding_executor,
+ EmbeddingCache* embedding_cache,
+ std::vector<float>* output_features) const {
+ // Look for the embedded features for the token in the cache, if there is one.
+ if (embedding_cache) {
+ const auto it = embedding_cache->find({token.start, token.end});
+ if (it != embedding_cache->end()) {
+ // The embedded features were found in the cache, extract only the dense
+ // features.
+ std::vector<float> dense_features;
+ if (!feature_extractor_.Extract(
+ token, token.IsContainedInSpan(selection_span_for_feature),
+ /*sparse_features=*/nullptr, &dense_features)) {
+ TC_LOG(ERROR) << "Could not extract token's dense features.";
+ return false;
+ }
+
+ // Append both embedded and dense features to the output and return.
+ output_features->insert(output_features->end(), it->second.begin(),
+ it->second.end());
+ output_features->insert(output_features->end(), dense_features.begin(),
+ dense_features.end());
+ return true;
+ }
+ }
+
+ // Extract the sparse and dense features.
+ std::vector<int> sparse_features;
+ std::vector<float> dense_features;
+ if (!feature_extractor_.Extract(
+ token, token.IsContainedInSpan(selection_span_for_feature),
+ &sparse_features, &dense_features)) {
+ TC_LOG(ERROR) << "Could not extract token's features.";
+ return false;
+ }
+
+ // Embed the sparse features, appending them directly to the output.
+ const int embedding_size = GetOptions()->embedding_size();
+ output_features->resize(output_features->size() + embedding_size);
+ float* output_features_end =
+ output_features->data() + output_features->size();
+ if (!embedding_executor->AddEmbedding(
+ TensorView<int>(sparse_features.data(),
+ {static_cast<int>(sparse_features.size())}),
+ /*dest=*/output_features_end - embedding_size,
+ /*dest_size=*/embedding_size)) {
+ TC_LOG(ERROR) << "Cound not embed token's sparse features.";
+ return false;
+ }
+
+ // If there is a cache, the embedded features for the token were not in it,
+ // so insert them.
+ if (embedding_cache) {
+ (*embedding_cache)[{token.start, token.end}] = std::vector<float>(
+ output_features_end - embedding_size, output_features_end);
+ }
+
+ // Append the dense features to the output.
+ output_features->insert(output_features->end(), dense_features.begin(),
+ dense_features.end());
+ return true;
+}
+
+} // namespace libtextclassifier2
diff --git a/smartselect/feature-processor.h b/feature-processor.h
similarity index 61%
rename from smartselect/feature-processor.h
rename to feature-processor.h
index ef9a3df..98d3449 100644
--- a/smartselect/feature-processor.h
+++ b/feature-processor.h
@@ -16,42 +16,33 @@
// Feature processing for FFModel (feed-forward SmartSelection model).
-#ifndef LIBTEXTCLASSIFIER_SMARTSELECT_FEATURE_PROCESSOR_H_
-#define LIBTEXTCLASSIFIER_SMARTSELECT_FEATURE_PROCESSOR_H_
+#ifndef LIBTEXTCLASSIFIER_FEATURE_PROCESSOR_H_
+#define LIBTEXTCLASSIFIER_FEATURE_PROCESSOR_H_
+#include <map>
#include <memory>
#include <set>
#include <string>
#include <vector>
-#include "smartselect/cached-features.h"
-#include "smartselect/text-classification-model.pb.h"
-#include "smartselect/token-feature-extractor.h"
-#include "smartselect/tokenizer.h"
-#include "smartselect/types.h"
+#include "cached-features.h"
+#include "model_generated.h"
+#include "token-feature-extractor.h"
+#include "tokenizer.h"
+#include "types.h"
+#include "util/base/integral_types.h"
#include "util/base/logging.h"
#include "util/utf8/unicodetext.h"
+#include "util/utf8/unilib.h"
-namespace libtextclassifier {
+namespace libtextclassifier2 {
constexpr int kInvalidLabel = -1;
-// Maps a vector of sparse features and a vector of dense features to a vector
-// of features that combines both.
-// The output is written to the memory location pointed to by the last float*
-// argument.
-// Returns true on success false on failure.
-using FeatureVectorFn = std::function<bool(const std::vector<int>&,
- const std::vector<float>&, float*)>;
-
namespace internal {
-// Parses the serialized protocol buffer.
-FeatureProcessorOptions ParseSerializedOptions(
- const std::string& serialized_options);
-
TokenFeatureExtractorOptions BuildTokenFeatureExtractorOptions(
- const FeatureProcessorOptions& options);
+ const FeatureProcessorOptions* options);
// Splits tokens that contain the selection boundary inside them.
// E.g. "foo{bar}@google.com" -> "foo", "bar", "@google.com"
@@ -73,6 +64,11 @@
void StripOrPadTokens(TokenSpan relative_click_span, int context_size,
std::vector<Token>* tokens, int* click_pos);
+// If unilib is not nullptr, just returns unilib. Otherwise, if unilib is
+// nullptr, will create UniLib, assign ownership to owned_unilib, and return it.
+const UniLib* MaybeCreateUnilib(const UniLib* unilib,
+ std::unique_ptr<UniLib>* owned_unilib);
+
} // namespace internal
// Converts a codepoint span to a token span in the given list of tokens.
@@ -90,29 +86,48 @@
// Takes care of preparing features for the span prediction model.
class FeatureProcessor {
public:
- explicit FeatureProcessor(const FeatureProcessorOptions& options)
- : feature_extractor_(
- internal::BuildTokenFeatureExtractorOptions(options)),
+ // A cache mapping codepoint spans to embedded tokens features. An instance
+ // can be provided to multiple calls to ExtractFeatures() operating on the
+ // same context (the same codepoint spans corresponding to the same tokens),
+ // as an optimization. Note that the tokenizations do not have to be
+ // identical.
+ typedef std::map<CodepointSpan, std::vector<float>> EmbeddingCache;
+
+ // If unilib is nullptr, will create and own an instance of a UniLib,
+ // otherwise will use what's passed in.
+ explicit FeatureProcessor(const FeatureProcessorOptions* options,
+ const UniLib* unilib = nullptr)
+ : owned_unilib_(nullptr),
+ unilib_(internal::MaybeCreateUnilib(unilib, &owned_unilib_)),
+ feature_extractor_(internal::BuildTokenFeatureExtractorOptions(options),
+ *unilib_),
options_(options),
- tokenizer_({options.tokenization_codepoint_config().begin(),
- options.tokenization_codepoint_config().end()}) {
+ tokenizer_(
+ options->tokenization_codepoint_config() != nullptr
+ ? Tokenizer({options->tokenization_codepoint_config()->begin(),
+ options->tokenization_codepoint_config()->end()},
+ options->tokenize_on_script_change())
+ : Tokenizer({}, /*split_on_script_change=*/false)) {
MakeLabelMaps();
- PrepareCodepointRanges({options.supported_codepoint_ranges().begin(),
- options.supported_codepoint_ranges().end()},
- &supported_codepoint_ranges_);
- PrepareCodepointRanges(
- {options.internal_tokenizer_codepoint_ranges().begin(),
- options.internal_tokenizer_codepoint_ranges().end()},
- &internal_tokenizer_codepoint_ranges_);
+ if (options->supported_codepoint_ranges() != nullptr) {
+ PrepareCodepointRanges({options->supported_codepoint_ranges()->begin(),
+ options->supported_codepoint_ranges()->end()},
+ &supported_codepoint_ranges_);
+ }
+ if (options->internal_tokenizer_codepoint_ranges() != nullptr) {
+ PrepareCodepointRanges(
+ {options->internal_tokenizer_codepoint_ranges()->begin(),
+ options->internal_tokenizer_codepoint_ranges()->end()},
+ &internal_tokenizer_codepoint_ranges_);
+ }
PrepareIgnoredSpanBoundaryCodepoints();
}
- explicit FeatureProcessor(const std::string& serialized_options)
- : FeatureProcessor(internal::ParseSerializedOptions(serialized_options)) {
- }
-
// Tokenizes the input string using the selected tokenization method.
- std::vector<Token> Tokenize(const std::string& utf8_text) const;
+ std::vector<Token> Tokenize(const std::string& text) const;
+
+ // Same as above but takes UnicodeText.
+ std::vector<Token> Tokenize(const UnicodeText& text_unicode) const;
// Converts a label into a token span.
bool LabelToTokenSpan(int label, TokenSpan* token_span) const;
@@ -129,22 +144,32 @@
// Gets the name of the default collection.
std::string GetDefaultCollection() const;
- const FeatureProcessorOptions& GetOptions() const { return options_; }
+ const FeatureProcessorOptions* GetOptions() const { return options_; }
- // Tokenizes the context and input span, and finds the click position.
- void TokenizeAndFindClick(const std::string& context,
- CodepointSpan input_span,
- std::vector<Token>* tokens, int* click_pos) const;
+ // Retokenizes the context and input span, and finds the click position.
+ // Depending on the options, might modify tokens (split them or remove them).
+ void RetokenizeAndFindClick(const std::string& context,
+ CodepointSpan input_span,
+ bool only_use_line_with_click,
+ std::vector<Token>* tokens, int* click_pos) const;
+
+ // Same as above but takes UnicodeText.
+ void RetokenizeAndFindClick(const UnicodeText& context_unicode,
+ CodepointSpan input_span,
+ bool only_use_line_with_click,
+ std::vector<Token>* tokens, int* click_pos) const;
+
+ // Returns true if the token span has enough supported codepoints (as defined
+ // in the model config) or not and model should not run.
+ bool HasEnoughSupportedCodepoints(const std::vector<Token>& tokens,
+ TokenSpan token_span) const;
// Extracts features as a CachedFeatures object that can be used for repeated
// inference over token spans in the given context.
- // When input_span == {kInvalidIndex, kInvalidIndex} then, relative_click_span
- // is ignored, and all tokens extracted from context will be considered.
- bool ExtractFeatures(const std::string& context, CodepointSpan input_span,
- TokenSpan relative_click_span,
- const FeatureVectorFn& feature_vector_fn,
- int feature_vector_size, std::vector<Token>* tokens,
- int* click_pos,
+ bool ExtractFeatures(const std::vector<Token>& tokens, TokenSpan token_span,
+ CodepointSpan selection_span_for_feature,
+ const EmbeddingExecutor* embedding_executor,
+ EmbeddingCache* embedding_cache, int feature_vector_size,
std::unique_ptr<CachedFeatures>* cached_features) const;
// Fills selection_label_spans with CodepointSpans that correspond to the
@@ -158,7 +183,9 @@
return feature_extractor_.DenseFeaturesCount();
}
- // Splits context to several segments according to configuration.
+ int EmbeddingSize() const { return options_->embedding_size(); }
+
+ // Splits context to several segments.
std::vector<UnicodeTextRange> SplitContext(
const UnicodeText& context_unicode) const;
@@ -168,6 +195,10 @@
CodepointSpan StripBoundaryCodepoints(const std::string& context,
CodepointSpan span) const;
+ // Same as above but takes UnicodeText.
+ CodepointSpan StripBoundaryCodepoints(const UnicodeText& context_unicode,
+ CodepointSpan span) const;
+
protected:
// Represents a codepoint range [start, end).
struct CodepointRange {
@@ -191,7 +222,7 @@
// Spannable tokens are those tokens of context, which the model predicts
// selection spans over (i.e., there is 1:1 correspondence between the output
// classes of the model and each of the spannable tokens).
- int GetNumContextTokens() const { return options_.context_size() * 2 + 1; }
+ int GetNumContextTokens() const { return options_->context_size() * 2 + 1; }
// Converts a label into a span of codepoint indices corresponding to it
// given output_tokens.
@@ -206,13 +237,13 @@
int TokenSpanToLabel(const std::pair<TokenIndex, TokenIndex>& span) const;
void PrepareCodepointRanges(
- const std::vector<FeatureProcessorOptions::CodepointRange>&
+ const std::vector<const FeatureProcessorOptions_::CodepointRange*>&
codepoint_ranges,
std::vector<CodepointRange>* prepared_codepoint_ranges);
// Returns the ratio of supported codepoints to total number of codepoints in
- // the input context around given click position.
- float SupportedCodepointsRatio(int click_pos,
+ // the given token span.
+ float SupportedCodepointsRatio(const TokenSpan& token_span,
const std::vector<Token>& tokens) const;
// Returns true if given codepoint is covered by the given sorted vector of
@@ -238,12 +269,12 @@
const std::vector<Token>& tokens) const;
// Tokenizes the input text using ICU tokenizer.
- bool ICUTokenize(const std::string& context,
+ bool ICUTokenize(const UnicodeText& context_unicode,
std::vector<Token>* result) const;
// Takes the result of ICU tokenization and retokenizes stretches of tokens
// made of a specific subset of characters using the internal tokenizer.
- void InternalRetokenize(const std::string& context,
+ void InternalRetokenize(const UnicodeText& unicode_text,
std::vector<Token>* tokens) const;
// Tokenizes a substring of the unicode string, appending the resulting tokens
@@ -257,6 +288,25 @@
void StripTokensFromOtherLines(const std::string& context, CodepointSpan span,
std::vector<Token>* tokens) const;
+ // Same as above but takes UnicodeText.
+ void StripTokensFromOtherLines(const UnicodeText& context_unicode,
+ CodepointSpan span,
+ std::vector<Token>* tokens) const;
+
+ // Extracts the features of a token and appends them to the output vector.
+ // Uses the embedding cache to to avoid re-extracting the re-embedding the
+ // sparse features for the same token.
+ bool AppendTokenFeaturesWithCache(const Token& token,
+ CodepointSpan selection_span_for_feature,
+ const EmbeddingExecutor* embedding_executor,
+ EmbeddingCache* embedding_cache,
+ std::vector<float>* output_features) const;
+
+ private:
+ std::unique_ptr<UniLib> owned_unilib_;
+ const UniLib* unilib_;
+
+ protected:
const TokenFeatureExtractor feature_extractor_;
// Codepoint ranges that define what codepoints are supported by the model.
@@ -274,7 +324,7 @@
// predicted spans.
std::set<int32> ignored_span_boundary_codepoints_;
- const FeatureProcessorOptions options_;
+ const FeatureProcessorOptions* const options_;
// Mapping between token selection spans and labels ids.
std::map<TokenSpan, int> selection_to_label_;
@@ -286,6 +336,6 @@
Tokenizer tokenizer_;
};
-} // namespace libtextclassifier
+} // namespace libtextclassifier2
-#endif // LIBTEXTCLASSIFIER_SMARTSELECT_FEATURE_PROCESSOR_H_
+#endif // LIBTEXTCLASSIFIER_FEATURE_PROCESSOR_H_
diff --git a/smartselect/feature-processor_test.cc b/feature-processor_test.cc
similarity index 61%
rename from smartselect/feature-processor_test.cc
rename to feature-processor_test.cc
index 9bee67a..58b3033 100644
--- a/smartselect/feature-processor_test.cc
+++ b/feature-processor_test.cc
@@ -14,16 +14,40 @@
* limitations under the License.
*/
-#include "smartselect/feature-processor.h"
+#include "feature-processor.h"
+
+#include "model-executor.h"
+#include "tensor-view.h"
#include "gmock/gmock.h"
#include "gtest/gtest.h"
-namespace libtextclassifier {
+namespace libtextclassifier2 {
namespace {
using testing::ElementsAreArray;
using testing::FloatEq;
+using testing::Matcher;
+
+flatbuffers::DetachedBuffer PackFeatureProcessorOptions(
+ const FeatureProcessorOptionsT& options) {
+ flatbuffers::FlatBufferBuilder builder;
+ builder.Finish(CreateFeatureProcessorOptions(builder, &options));
+ return builder.Release();
+}
+
+template <typename T>
+std::vector<T> Subvector(const std::vector<T>& vector, int start, int end) {
+ return std::vector<T>(vector.begin() + start, vector.begin() + end);
+}
+
+Matcher<std::vector<float>> ElementsAreFloat(const std::vector<float>& values) {
+ std::vector<Matcher<float>> matchers;
+ for (const float value : values) {
+ matchers.push_back(FloatEq(value));
+ }
+ return ElementsAreArray(matchers);
+}
class TestingFeatureProcessor : public FeatureProcessor {
public:
@@ -37,6 +61,24 @@
using FeatureProcessor::SupportedCodepointsRatio;
};
+// EmbeddingExecutor that always returns features based on
+class FakeEmbeddingExecutor : public EmbeddingExecutor {
+ public:
+ bool AddEmbedding(const TensorView<int>& sparse_features, float* dest,
+ int dest_size) const override {
+ TC_CHECK_GE(dest_size, 4);
+ EXPECT_EQ(sparse_features.size(), 1);
+ dest[0] = sparse_features.data()[0];
+ dest[1] = sparse_features.data()[0];
+ dest[2] = -sparse_features.data()[0];
+ dest[3] = -sparse_features.data()[0];
+ return true;
+ }
+
+ private:
+ std::vector<float> storage_;
+};
+
TEST(FeatureProcessorTest, SplitTokensOnSelectionBoundariesMiddle) {
std::vector<Token> tokens{Token("Hělló", 0, 5),
Token("fěěbař@google.com", 6, 23),
@@ -119,9 +161,13 @@
}
TEST(FeatureProcessorTest, KeepLineWithClickFirst) {
- FeatureProcessorOptions options;
- options.set_only_use_line_with_click(true);
- TestingFeatureProcessor feature_processor(options);
+ CREATE_UNILIB_FOR_TESTING;
+ FeatureProcessorOptionsT options;
+ options.only_use_line_with_click = true;
+ flatbuffers::DetachedBuffer options_fb = PackFeatureProcessorOptions(options);
+ TestingFeatureProcessor feature_processor(
+ flatbuffers::GetRoot<FeatureProcessorOptions>(options_fb.data()),
+ &unilib);
const std::string context = "Fiřst Lině\nSěcond Lině\nThiřd Lině";
const CodepointSpan span = {0, 5};
@@ -141,9 +187,13 @@
}
TEST(FeatureProcessorTest, KeepLineWithClickSecond) {
- FeatureProcessorOptions options;
- options.set_only_use_line_with_click(true);
- TestingFeatureProcessor feature_processor(options);
+ CREATE_UNILIB_FOR_TESTING;
+ FeatureProcessorOptionsT options;
+ options.only_use_line_with_click = true;
+ flatbuffers::DetachedBuffer options_fb = PackFeatureProcessorOptions(options);
+ TestingFeatureProcessor feature_processor(
+ flatbuffers::GetRoot<FeatureProcessorOptions>(options_fb.data()),
+ &unilib);
const std::string context = "Fiřst Lině\nSěcond Lině\nThiřd Lině";
const CodepointSpan span = {18, 22};
@@ -163,9 +213,13 @@
}
TEST(FeatureProcessorTest, KeepLineWithClickThird) {
- FeatureProcessorOptions options;
- options.set_only_use_line_with_click(true);
- TestingFeatureProcessor feature_processor(options);
+ CREATE_UNILIB_FOR_TESTING;
+ FeatureProcessorOptionsT options;
+ options.only_use_line_with_click = true;
+ flatbuffers::DetachedBuffer options_fb = PackFeatureProcessorOptions(options);
+ TestingFeatureProcessor feature_processor(
+ flatbuffers::GetRoot<FeatureProcessorOptions>(options_fb.data()),
+ &unilib);
const std::string context = "Fiřst Lině\nSěcond Lině\nThiřd Lině";
const CodepointSpan span = {24, 33};
@@ -185,9 +239,13 @@
}
TEST(FeatureProcessorTest, KeepLineWithClickSecondWithPipe) {
- FeatureProcessorOptions options;
- options.set_only_use_line_with_click(true);
- TestingFeatureProcessor feature_processor(options);
+ CREATE_UNILIB_FOR_TESTING;
+ FeatureProcessorOptionsT options;
+ options.only_use_line_with_click = true;
+ flatbuffers::DetachedBuffer options_fb = PackFeatureProcessorOptions(options);
+ TestingFeatureProcessor feature_processor(
+ flatbuffers::GetRoot<FeatureProcessorOptions>(options_fb.data()),
+ &unilib);
const std::string context = "Fiřst Lině|Sěcond Lině\nThiřd Lině";
const CodepointSpan span = {18, 22};
@@ -207,9 +265,13 @@
}
TEST(FeatureProcessorTest, KeepLineWithCrosslineClick) {
- FeatureProcessorOptions options;
- options.set_only_use_line_with_click(true);
- TestingFeatureProcessor feature_processor(options);
+ CREATE_UNILIB_FOR_TESTING;
+ FeatureProcessorOptionsT options;
+ options.only_use_line_with_click = true;
+ flatbuffers::DetachedBuffer options_fb = PackFeatureProcessorOptions(options);
+ TestingFeatureProcessor feature_processor(
+ flatbuffers::GetRoot<FeatureProcessorOptions>(options_fb.data()),
+ &unilib);
const std::string context = "Fiřst Lině\nSěcond Lině\nThiřd Lině";
const CodepointSpan span = {5, 23};
@@ -231,18 +293,23 @@
}
TEST(FeatureProcessorTest, SpanToLabel) {
- FeatureProcessorOptions options;
- options.set_context_size(1);
- options.set_max_selection_span(1);
- options.set_snap_label_span_boundaries_to_containing_tokens(false);
+ CREATE_UNILIB_FOR_TESTING;
+ FeatureProcessorOptionsT options;
+ options.context_size = 1;
+ options.max_selection_span = 1;
+ options.snap_label_span_boundaries_to_containing_tokens = false;
- TokenizationCodepointRange* config =
- options.add_tokenization_codepoint_config();
- config->set_start(32);
- config->set_end(33);
- config->set_role(TokenizationCodepointRange::WHITESPACE_SEPARATOR);
+ options.tokenization_codepoint_config.emplace_back(
+ new TokenizationCodepointRangeT());
+ auto& config = options.tokenization_codepoint_config.back();
+ config->start = 32;
+ config->end = 33;
+ config->role = TokenizationCodepointRange_::Role_WHITESPACE_SEPARATOR;
- TestingFeatureProcessor feature_processor(options);
+ flatbuffers::DetachedBuffer options_fb = PackFeatureProcessorOptions(options);
+ TestingFeatureProcessor feature_processor(
+ flatbuffers::GetRoot<FeatureProcessorOptions>(options_fb.data()),
+ &unilib);
std::vector<Token> tokens = feature_processor.Tokenize("one, two, three");
ASSERT_EQ(3, tokens.size());
int label;
@@ -256,8 +323,12 @@
EXPECT_EQ(0, token_span.second);
// Reconfigure with snapping enabled.
- options.set_snap_label_span_boundaries_to_containing_tokens(true);
- TestingFeatureProcessor feature_processor2(options);
+ options.snap_label_span_boundaries_to_containing_tokens = true;
+ flatbuffers::DetachedBuffer options2_fb =
+ PackFeatureProcessorOptions(options);
+ TestingFeatureProcessor feature_processor2(
+ flatbuffers::GetRoot<FeatureProcessorOptions>(options2_fb.data()),
+ &unilib);
int label2;
ASSERT_TRUE(feature_processor2.SpanToLabel({5, 8}, tokens, &label2));
EXPECT_EQ(label, label2);
@@ -273,9 +344,13 @@
EXPECT_EQ(kInvalidLabel, label2);
// Multiple tokens.
- options.set_context_size(2);
- options.set_max_selection_span(2);
- TestingFeatureProcessor feature_processor3(options);
+ options.context_size = 2;
+ options.max_selection_span = 2;
+ flatbuffers::DetachedBuffer options3_fb =
+ PackFeatureProcessorOptions(options);
+ TestingFeatureProcessor feature_processor3(
+ flatbuffers::GetRoot<FeatureProcessorOptions>(options3_fb.data()),
+ &unilib);
tokens = feature_processor3.Tokenize("zero, one, two, three, four");
ASSERT_TRUE(feature_processor3.SpanToLabel({6, 15}, tokens, &label2));
EXPECT_NE(kInvalidLabel, label2);
@@ -293,18 +368,23 @@
}
TEST(FeatureProcessorTest, SpanToLabelIgnoresPunctuation) {
- FeatureProcessorOptions options;
- options.set_context_size(1);
- options.set_max_selection_span(1);
- options.set_snap_label_span_boundaries_to_containing_tokens(false);
+ CREATE_UNILIB_FOR_TESTING;
+ FeatureProcessorOptionsT options;
+ options.context_size = 1;
+ options.max_selection_span = 1;
+ options.snap_label_span_boundaries_to_containing_tokens = false;
- TokenizationCodepointRange* config =
- options.add_tokenization_codepoint_config();
- config->set_start(32);
- config->set_end(33);
- config->set_role(TokenizationCodepointRange::WHITESPACE_SEPARATOR);
+ options.tokenization_codepoint_config.emplace_back(
+ new TokenizationCodepointRangeT());
+ auto& config = options.tokenization_codepoint_config.back();
+ config->start = 32;
+ config->end = 33;
+ config->role = TokenizationCodepointRange_::Role_WHITESPACE_SEPARATOR;
- TestingFeatureProcessor feature_processor(options);
+ flatbuffers::DetachedBuffer options_fb = PackFeatureProcessorOptions(options);
+ TestingFeatureProcessor feature_processor(
+ flatbuffers::GetRoot<FeatureProcessorOptions>(options_fb.data()),
+ &unilib);
std::vector<Token> tokens = feature_processor.Tokenize("one, two, three");
ASSERT_EQ(3, tokens.size());
int label;
@@ -318,8 +398,12 @@
EXPECT_EQ(0, token_span.second);
// Reconfigure with snapping enabled.
- options.set_snap_label_span_boundaries_to_containing_tokens(true);
- TestingFeatureProcessor feature_processor2(options);
+ options.snap_label_span_boundaries_to_containing_tokens = true;
+ flatbuffers::DetachedBuffer options2_fb =
+ PackFeatureProcessorOptions(options);
+ TestingFeatureProcessor feature_processor2(
+ flatbuffers::GetRoot<FeatureProcessorOptions>(options2_fb.data()),
+ &unilib);
int label2;
ASSERT_TRUE(feature_processor2.SpanToLabel({5, 8}, tokens, &label2));
EXPECT_EQ(label, label2);
@@ -335,9 +419,13 @@
EXPECT_EQ(kInvalidLabel, label2);
// Multiple tokens.
- options.set_context_size(2);
- options.set_max_selection_span(2);
- TestingFeatureProcessor feature_processor3(options);
+ options.context_size = 2;
+ options.max_selection_span = 2;
+ flatbuffers::DetachedBuffer options3_fb =
+ PackFeatureProcessorOptions(options);
+ TestingFeatureProcessor feature_processor3(
+ flatbuffers::GetRoot<FeatureProcessorOptions>(options3_fb.data()),
+ &unilib);
tokens = feature_processor3.Tokenize("zero, one, two, three, four");
ASSERT_TRUE(feature_processor3.SpanToLabel({6, 15}, tokens, &label2));
EXPECT_NE(kInvalidLabel, label2);
@@ -420,39 +508,66 @@
}
TEST(FeatureProcessorTest, SupportedCodepointsRatio) {
- FeatureProcessorOptions options;
- options.set_context_size(2);
- options.set_max_selection_span(2);
- options.set_snap_label_span_boundaries_to_containing_tokens(false);
+ FeatureProcessorOptionsT options;
+ options.context_size = 2;
+ options.max_selection_span = 2;
+ options.snap_label_span_boundaries_to_containing_tokens = false;
+ options.feature_version = 2;
+ options.embedding_size = 4;
+ options.bounds_sensitive_features.reset(
+ new FeatureProcessorOptions_::BoundsSensitiveFeaturesT());
+ options.bounds_sensitive_features->enabled = true;
+ options.bounds_sensitive_features->num_tokens_before = 5;
+ options.bounds_sensitive_features->num_tokens_inside_left = 3;
+ options.bounds_sensitive_features->num_tokens_inside_right = 3;
+ options.bounds_sensitive_features->num_tokens_after = 5;
+ options.bounds_sensitive_features->include_inside_bag = true;
+ options.bounds_sensitive_features->include_inside_length = true;
- TokenizationCodepointRange* config =
- options.add_tokenization_codepoint_config();
- config->set_start(32);
- config->set_end(33);
- config->set_role(TokenizationCodepointRange::WHITESPACE_SEPARATOR);
+ options.tokenization_codepoint_config.emplace_back(
+ new TokenizationCodepointRangeT());
+ auto& config = options.tokenization_codepoint_config.back();
+ config->start = 32;
+ config->end = 33;
+ config->role = TokenizationCodepointRange_::Role_WHITESPACE_SEPARATOR;
- FeatureProcessorOptions::CodepointRange* range;
- range = options.add_supported_codepoint_ranges();
- range->set_start(0);
- range->set_end(128);
+ {
+ options.supported_codepoint_ranges.emplace_back(
+ new FeatureProcessorOptions_::CodepointRangeT());
+ auto& range = options.supported_codepoint_ranges.back();
+ range->start = 0;
+ range->end = 128;
+ }
- range = options.add_supported_codepoint_ranges();
- range->set_start(10000);
- range->set_end(10001);
+ {
+ options.supported_codepoint_ranges.emplace_back(
+ new FeatureProcessorOptions_::CodepointRangeT());
+ auto& range = options.supported_codepoint_ranges.back();
+ range->start = 10000;
+ range->end = 10001;
+ }
- range = options.add_supported_codepoint_ranges();
- range->set_start(20000);
- range->set_end(30000);
+ {
+ options.supported_codepoint_ranges.emplace_back(
+ new FeatureProcessorOptions_::CodepointRangeT());
+ auto& range = options.supported_codepoint_ranges.back();
+ range->start = 20000;
+ range->end = 30000;
+ }
- TestingFeatureProcessor feature_processor(options);
+ flatbuffers::DetachedBuffer options_fb = PackFeatureProcessorOptions(options);
+ CREATE_UNILIB_FOR_TESTING;
+ TestingFeatureProcessor feature_processor(
+ flatbuffers::GetRoot<FeatureProcessorOptions>(options_fb.data()),
+ &unilib);
EXPECT_THAT(feature_processor.SupportedCodepointsRatio(
- 1, feature_processor.Tokenize("aaa bbb ccc")),
+ {0, 3}, feature_processor.Tokenize("aaa bbb ccc")),
FloatEq(1.0));
EXPECT_THAT(feature_processor.SupportedCodepointsRatio(
- 1, feature_processor.Tokenize("aaa bbb ěěě")),
+ {0, 3}, feature_processor.Tokenize("aaa bbb ěěě")),
FloatEq(2.0 / 3));
EXPECT_THAT(feature_processor.SupportedCodepointsRatio(
- 1, feature_processor.Tokenize("ěěě řřř ěěě")),
+ {0, 3}, feature_processor.Tokenize("ěěě řřř ěěě")),
FloatEq(0.0));
EXPECT_FALSE(feature_processor.IsCodepointInRanges(
-1, feature_processor.supported_codepoint_ranges_));
@@ -473,32 +588,142 @@
EXPECT_TRUE(feature_processor.IsCodepointInRanges(
25000, feature_processor.supported_codepoint_ranges_));
- std::vector<Token> tokens;
- int click_pos;
- std::vector<float> extra_features;
+ const std::vector<Token> tokens = {Token("ěěě", 0, 3), Token("řřř", 4, 7),
+ Token("eee", 8, 11)};
+
+ options.min_supported_codepoint_ratio = 0.0;
+ flatbuffers::DetachedBuffer options2_fb =
+ PackFeatureProcessorOptions(options);
+ TestingFeatureProcessor feature_processor2(
+ flatbuffers::GetRoot<FeatureProcessorOptions>(options2_fb.data()),
+ &unilib);
+ EXPECT_TRUE(feature_processor2.HasEnoughSupportedCodepoints(
+ tokens, /*token_span=*/{0, 3}));
+
+ options.min_supported_codepoint_ratio = 0.2;
+ flatbuffers::DetachedBuffer options3_fb =
+ PackFeatureProcessorOptions(options);
+ TestingFeatureProcessor feature_processor3(
+ flatbuffers::GetRoot<FeatureProcessorOptions>(options3_fb.data()),
+ &unilib);
+ EXPECT_TRUE(feature_processor3.HasEnoughSupportedCodepoints(
+ tokens, /*token_span=*/{0, 3}));
+
+ options.min_supported_codepoint_ratio = 0.5;
+ flatbuffers::DetachedBuffer options4_fb =
+ PackFeatureProcessorOptions(options);
+ TestingFeatureProcessor feature_processor4(
+ flatbuffers::GetRoot<FeatureProcessorOptions>(options4_fb.data()),
+ &unilib);
+ EXPECT_FALSE(feature_processor4.HasEnoughSupportedCodepoints(
+ tokens, /*token_span=*/{0, 3}));
+}
+
+TEST(FeatureProcessorTest, InSpanFeature) {
+ FeatureProcessorOptionsT options;
+ options.context_size = 2;
+ options.max_selection_span = 2;
+ options.snap_label_span_boundaries_to_containing_tokens = false;
+ options.feature_version = 2;
+ options.embedding_size = 4;
+ options.extract_selection_mask_feature = true;
+
+ flatbuffers::DetachedBuffer options_fb = PackFeatureProcessorOptions(options);
+ CREATE_UNILIB_FOR_TESTING;
+ TestingFeatureProcessor feature_processor(
+ flatbuffers::GetRoot<FeatureProcessorOptions>(options_fb.data()),
+ &unilib);
+
std::unique_ptr<CachedFeatures> cached_features;
- auto feature_fn = [](const std::vector<int>& sparse_features,
- const std::vector<float>& dense_features,
- float* embedding) { return true; };
+ FakeEmbeddingExecutor embedding_executor;
- options.set_min_supported_codepoint_ratio(0.0);
- TestingFeatureProcessor feature_processor2(options);
- EXPECT_TRUE(feature_processor2.ExtractFeatures("ěěě řřř eee", {4, 7}, {0, 0},
- feature_fn, 2, &tokens,
- &click_pos, &cached_features));
+ const std::vector<Token> tokens = {Token("aaa", 0, 3), Token("bbb", 4, 7),
+ Token("ccc", 8, 11), Token("ddd", 12, 15)};
- options.set_min_supported_codepoint_ratio(0.2);
- TestingFeatureProcessor feature_processor3(options);
- EXPECT_TRUE(feature_processor3.ExtractFeatures("ěěě řřř eee", {4, 7}, {0, 0},
- feature_fn, 2, &tokens,
- &click_pos, &cached_features));
-
- options.set_min_supported_codepoint_ratio(0.5);
- TestingFeatureProcessor feature_processor4(options);
- EXPECT_FALSE(feature_processor4.ExtractFeatures(
- "ěěě řřř eee", {4, 7}, {0, 0}, feature_fn, 2, &tokens, &click_pos,
+ EXPECT_TRUE(feature_processor.ExtractFeatures(
+ tokens, /*token_span=*/{0, 4},
+ /*selection_span_for_feature=*/{4, 11}, &embedding_executor,
+ /*embedding_cache=*/nullptr, /*feature_vector_size=*/5,
&cached_features));
+ std::vector<float> features;
+ cached_features->AppendClickContextFeaturesForClick(1, &features);
+ ASSERT_EQ(features.size(), 25);
+ EXPECT_THAT(features[4], FloatEq(0.0));
+ EXPECT_THAT(features[9], FloatEq(0.0));
+ EXPECT_THAT(features[14], FloatEq(1.0));
+ EXPECT_THAT(features[19], FloatEq(1.0));
+ EXPECT_THAT(features[24], FloatEq(0.0));
+}
+
+TEST(FeatureProcessorTest, EmbeddingCache) {
+ FeatureProcessorOptionsT options;
+ options.context_size = 2;
+ options.max_selection_span = 2;
+ options.snap_label_span_boundaries_to_containing_tokens = false;
+ options.feature_version = 2;
+ options.embedding_size = 4;
+ options.bounds_sensitive_features.reset(
+ new FeatureProcessorOptions_::BoundsSensitiveFeaturesT());
+ options.bounds_sensitive_features->enabled = true;
+ options.bounds_sensitive_features->num_tokens_before = 3;
+ options.bounds_sensitive_features->num_tokens_inside_left = 2;
+ options.bounds_sensitive_features->num_tokens_inside_right = 2;
+ options.bounds_sensitive_features->num_tokens_after = 3;
+
+ flatbuffers::DetachedBuffer options_fb = PackFeatureProcessorOptions(options);
+ CREATE_UNILIB_FOR_TESTING;
+ TestingFeatureProcessor feature_processor(
+ flatbuffers::GetRoot<FeatureProcessorOptions>(options_fb.data()),
+ &unilib);
+
+ std::unique_ptr<CachedFeatures> cached_features;
+
+ FakeEmbeddingExecutor embedding_executor;
+
+ const std::vector<Token> tokens = {
+ Token("aaa", 0, 3), Token("bbb", 4, 7), Token("ccc", 8, 11),
+ Token("ddd", 12, 15), Token("eee", 16, 19), Token("fff", 20, 23)};
+
+ // We pre-populate the cache with dummy embeddings, to make sure they are
+ // used when populating the features vector.
+ const std::vector<float> cached_padding_features = {10.0, -10.0, 10.0, -10.0};
+ const std::vector<float> cached_features1 = {1.0, 2.0, 3.0, 4.0};
+ const std::vector<float> cached_features2 = {5.0, 6.0, 7.0, 8.0};
+ FeatureProcessor::EmbeddingCache embedding_cache = {
+ {{kInvalidIndex, kInvalidIndex}, cached_padding_features},
+ {{4, 7}, cached_features1},
+ {{12, 15}, cached_features2},
+ };
+
+ EXPECT_TRUE(feature_processor.ExtractFeatures(
+ tokens, /*token_span=*/{0, 6},
+ /*selection_span_for_feature=*/{kInvalidIndex, kInvalidIndex},
+ &embedding_executor, &embedding_cache, /*feature_vector_size=*/4,
+ &cached_features));
+ std::vector<float> features;
+ cached_features->AppendBoundsSensitiveFeaturesForSpan({2, 4}, &features);
+ ASSERT_EQ(features.size(), 40);
+ // Check that the dummy embeddings were used.
+ EXPECT_THAT(Subvector(features, 0, 4),
+ ElementsAreFloat(cached_padding_features));
+ EXPECT_THAT(Subvector(features, 8, 12), ElementsAreFloat(cached_features1));
+ EXPECT_THAT(Subvector(features, 16, 20), ElementsAreFloat(cached_features2));
+ EXPECT_THAT(Subvector(features, 24, 28), ElementsAreFloat(cached_features2));
+ EXPECT_THAT(Subvector(features, 36, 40),
+ ElementsAreFloat(cached_padding_features));
+ // Check that the real embeddings were cached.
+ EXPECT_EQ(embedding_cache.size(), 7);
+ EXPECT_THAT(Subvector(features, 4, 8),
+ ElementsAreFloat(embedding_cache.at({0, 3})));
+ EXPECT_THAT(Subvector(features, 12, 16),
+ ElementsAreFloat(embedding_cache.at({8, 11})));
+ EXPECT_THAT(Subvector(features, 20, 24),
+ ElementsAreFloat(embedding_cache.at({8, 11})));
+ EXPECT_THAT(Subvector(features, 28, 32),
+ ElementsAreFloat(embedding_cache.at({16, 19})));
+ EXPECT_THAT(Subvector(features, 32, 36),
+ ElementsAreFloat(embedding_cache.at({20, 23})));
}
TEST(FeatureProcessorTest, StripUnusedTokensWithNoRelativeClick) {
@@ -613,12 +838,48 @@
EXPECT_EQ(click_index, 5);
}
-TEST(FeatureProcessorTest, ICUTokenize) {
- FeatureProcessorOptions options;
- options.set_tokenization_type(
- libtextclassifier::FeatureProcessorOptions::ICU);
+TEST(FeatureProcessorTest, InternalTokenizeOnScriptChange) {
+ CREATE_UNILIB_FOR_TESTING;
+ FeatureProcessorOptionsT options;
+ options.tokenization_codepoint_config.emplace_back(
+ new TokenizationCodepointRangeT());
+ {
+ auto& config = options.tokenization_codepoint_config.back();
+ config->start = 0;
+ config->end = 256;
+ config->role = TokenizationCodepointRange_::Role_DEFAULT_ROLE;
+ config->script_id = 1;
+ }
+ options.tokenize_on_script_change = false;
- TestingFeatureProcessor feature_processor(options);
+ flatbuffers::DetachedBuffer options_fb = PackFeatureProcessorOptions(options);
+ TestingFeatureProcessor feature_processor(
+ flatbuffers::GetRoot<FeatureProcessorOptions>(options_fb.data()),
+ &unilib);
+
+ EXPECT_EQ(feature_processor.Tokenize("앨라배마123웹사이트"),
+ std::vector<Token>({Token("앨라배마123웹사이트", 0, 11)}));
+
+ options.tokenize_on_script_change = true;
+ flatbuffers::DetachedBuffer options_fb2 =
+ PackFeatureProcessorOptions(options);
+ TestingFeatureProcessor feature_processor2(
+ flatbuffers::GetRoot<FeatureProcessorOptions>(options_fb2.data()),
+ &unilib);
+
+ EXPECT_EQ(feature_processor2.Tokenize("앨라배마123웹사이트"),
+ std::vector<Token>({Token("앨라배마", 0, 4), Token("123", 4, 7),
+ Token("웹사이트", 7, 11)}));
+}
+
+#ifdef LIBTEXTCLASSIFIER_TEST_ICU
+TEST(FeatureProcessorTest, ICUTokenize) {
+ FeatureProcessorOptionsT options;
+ options.tokenization_type = FeatureProcessorOptions_::TokenizationType_ICU;
+
+ flatbuffers::DetachedBuffer options_fb = PackFeatureProcessorOptions(options);
+ TestingFeatureProcessor feature_processor(
+ flatbuffers::GetRoot<FeatureProcessorOptions>(options_fb.data()));
std::vector<Token> tokens = feature_processor.Tokenize("พระบาทสมเด็จพระปรมิ");
ASSERT_EQ(tokens,
// clang-format off
@@ -629,14 +890,17 @@
Token("มิ", 17, 19)}));
// clang-format on
}
+#endif
+#ifdef LIBTEXTCLASSIFIER_TEST_ICU
TEST(FeatureProcessorTest, ICUTokenizeWithWhitespaces) {
- FeatureProcessorOptions options;
- options.set_tokenization_type(
- libtextclassifier::FeatureProcessorOptions::ICU);
- options.set_icu_preserve_whitespace_tokens(true);
+ FeatureProcessorOptionsT options;
+ options.tokenization_type = FeatureProcessorOptions_::TokenizationType_ICU;
+ options.icu_preserve_whitespace_tokens = true;
- TestingFeatureProcessor feature_processor(options);
+ flatbuffers::DetachedBuffer options_fb = PackFeatureProcessorOptions(options);
+ TestingFeatureProcessor feature_processor(
+ flatbuffers::GetRoot<FeatureProcessorOptions>(options_fb.data()));
std::vector<Token> tokens =
feature_processor.Tokenize("พระบาท สมเด็จ พระ ปร มิ");
ASSERT_EQ(tokens,
@@ -652,36 +916,55 @@
Token("มิ", 21, 23)}));
// clang-format on
}
+#endif
+#ifdef LIBTEXTCLASSIFIER_TEST_ICU
TEST(FeatureProcessorTest, MixedTokenize) {
- FeatureProcessorOptions options;
- options.set_tokenization_type(
- libtextclassifier::FeatureProcessorOptions::MIXED);
+ FeatureProcessorOptionsT options;
+ options.tokenization_type = FeatureProcessorOptions_::TokenizationType_MIXED;
- TokenizationCodepointRange* config =
- options.add_tokenization_codepoint_config();
- config->set_start(32);
- config->set_end(33);
- config->set_role(TokenizationCodepointRange::WHITESPACE_SEPARATOR);
+ options.tokenization_codepoint_config.emplace_back(
+ new TokenizationCodepointRangeT());
+ auto& config = options.tokenization_codepoint_config.back();
+ config->start = 32;
+ config->end = 33;
+ config->role = TokenizationCodepointRange_::Role_WHITESPACE_SEPARATOR;
- FeatureProcessorOptions::CodepointRange* range;
- range = options.add_internal_tokenizer_codepoint_ranges();
- range->set_start(0);
- range->set_end(128);
+ {
+ options.internal_tokenizer_codepoint_ranges.emplace_back(
+ new FeatureProcessorOptions_::CodepointRangeT());
+ auto& range = options.internal_tokenizer_codepoint_ranges.back();
+ range->start = 0;
+ range->end = 128;
+ }
- range = options.add_internal_tokenizer_codepoint_ranges();
- range->set_start(128);
- range->set_end(256);
+ {
+ options.internal_tokenizer_codepoint_ranges.emplace_back(
+ new FeatureProcessorOptions_::CodepointRangeT());
+ auto& range = options.internal_tokenizer_codepoint_ranges.back();
+ range->start = 128;
+ range->end = 256;
+ }
- range = options.add_internal_tokenizer_codepoint_ranges();
- range->set_start(256);
- range->set_end(384);
+ {
+ options.internal_tokenizer_codepoint_ranges.emplace_back(
+ new FeatureProcessorOptions_::CodepointRangeT());
+ auto& range = options.internal_tokenizer_codepoint_ranges.back();
+ range->start = 256;
+ range->end = 384;
+ }
- range = options.add_internal_tokenizer_codepoint_ranges();
- range->set_start(384);
- range->set_end(592);
+ {
+ options.internal_tokenizer_codepoint_ranges.emplace_back(
+ new FeatureProcessorOptions_::CodepointRangeT());
+ auto& range = options.internal_tokenizer_codepoint_ranges.back();
+ range->start = 384;
+ range->end = 592;
+ }
- TestingFeatureProcessor feature_processor(options);
+ flatbuffers::DetachedBuffer options_fb = PackFeatureProcessorOptions(options);
+ TestingFeatureProcessor feature_processor(
+ flatbuffers::GetRoot<FeatureProcessorOptions>(options_fb.data()));
std::vector<Token> tokens = feature_processor.Tokenize(
"こんにちはJapanese-ląnguagę text 世界 http://www.google.com/");
ASSERT_EQ(tokens,
@@ -693,15 +976,20 @@
Token("http://www.google.com/", 31, 53)}));
// clang-format on
}
+#endif
TEST(FeatureProcessorTest, IgnoredSpanBoundaryCodepoints) {
- FeatureProcessorOptions options;
- options.add_ignored_span_boundary_codepoints('.');
- options.add_ignored_span_boundary_codepoints(',');
- options.add_ignored_span_boundary_codepoints('[');
- options.add_ignored_span_boundary_codepoints(']');
+ CREATE_UNILIB_FOR_TESTING;
+ FeatureProcessorOptionsT options;
+ options.ignored_span_boundary_codepoints.push_back('.');
+ options.ignored_span_boundary_codepoints.push_back(',');
+ options.ignored_span_boundary_codepoints.push_back('[');
+ options.ignored_span_boundary_codepoints.push_back(']');
- TestingFeatureProcessor feature_processor(options);
+ flatbuffers::DetachedBuffer options_fb = PackFeatureProcessorOptions(options);
+ TestingFeatureProcessor feature_processor(
+ flatbuffers::GetRoot<FeatureProcessorOptions>(options_fb.data()),
+ &unilib);
const std::string text1_utf8 = "ěščř";
const UnicodeText text1 = UTF8ToUnicodeText(text1_utf8, /*do_copy=*/false);
@@ -834,4 +1122,4 @@
}
} // namespace
-} // namespace libtextclassifier
+} // namespace libtextclassifier2
diff --git a/jni.lds b/jni.lds
index 75d5bc5..171cc0e 100644
--- a/jni.lds
+++ b/jni.lds
@@ -1,7 +1,7 @@
-{
- # Export symbols that correspond to our JNIEXPORTed functions.
+VERS_1.0 {
+ # Export JNI symbols.
global:
- Java_android_view_textclassifier_*;
+ Java_*;
# Hide everything else.
local:
diff --git a/lang_id/custom-tokenizer.cc b/lang_id/custom-tokenizer.cc
deleted file mode 100644
index 7e30cc7..0000000
--- a/lang_id/custom-tokenizer.cc
+++ /dev/null
@@ -1,109 +0,0 @@
-/*
- * Copyright (C) 2017 The Android Open Source Project
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#include "lang_id/custom-tokenizer.h"
-
-#include <ctype.h>
-
-#include <string>
-
-#include "util/strings/utf8.h"
-
-namespace libtextclassifier {
-namespace nlp_core {
-namespace lang_id {
-
-namespace {
-inline bool IsTokenSeparator(int num_bytes, const char *curr) {
- if (num_bytes != 1) {
- return false;
- }
- return !isalpha(*curr);
-}
-} // namespace
-
-const char *GetSafeEndOfString(const char *data, size_t size) {
- const char *const hard_end = data + size;
- const char *curr = data;
- while (curr < hard_end) {
- int num_bytes = GetNumBytesForUTF8Char(curr);
- if (num_bytes == 0) {
- break;
- }
- const char *new_curr = curr + num_bytes;
- if (new_curr > hard_end) {
- return curr;
- }
- curr = new_curr;
- }
- return curr;
-}
-
-void TokenizeTextForLangId(const std::string &text, LightSentence *sentence) {
- const char *const start = text.data();
- const char *curr = start;
- const char *end = GetSafeEndOfString(start, text.size());
-
- // Corner case: empty safe part of the text.
- if (curr >= end) {
- return;
- }
-
- // Number of bytes for UTF8 character starting at *curr. Note: the loop below
- // is guaranteed to terminate because in each iteration, we move curr by at
- // least num_bytes, and num_bytes is guaranteed to be > 0.
- int num_bytes = GetNumBytesForNonZeroUTF8Char(curr);
- while (curr < end) {
- // Jump over consecutive token separators.
- while (IsTokenSeparator(num_bytes, curr)) {
- curr += num_bytes;
- if (curr >= end) {
- return;
- }
- num_bytes = GetNumBytesForNonZeroUTF8Char(curr);
- }
-
- // If control reaches this point, we are at beginning of a non-empty token.
- std::string *word = sentence->add_word();
-
- // Add special token-start character.
- word->push_back('^');
-
- // Add UTF8 characters to word, until we hit the end of the safe text or a
- // token separator.
- while (true) {
- word->append(curr, num_bytes);
- curr += num_bytes;
- if (curr >= end) {
- break;
- }
- num_bytes = GetNumBytesForNonZeroUTF8Char(curr);
- if (IsTokenSeparator(num_bytes, curr)) {
- curr += num_bytes;
- num_bytes = GetNumBytesForNonZeroUTF8Char(curr);
- break;
- }
- }
- word->push_back('$');
-
- // Note: we intentionally do not token.set_start()/end(), as those fields
- // are not used by the langid model.
- }
-}
-
-} // namespace lang_id
-} // namespace nlp_core
-} // namespace libtextclassifier
diff --git a/lang_id/custom-tokenizer.h b/lang_id/custom-tokenizer.h
deleted file mode 100644
index c9c291c..0000000
--- a/lang_id/custom-tokenizer.h
+++ /dev/null
@@ -1,62 +0,0 @@
-/*
- * Copyright (C) 2017 The Android Open Source Project
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#ifndef LIBTEXTCLASSIFIER_LANG_ID_CUSTOM_TOKENIZER_H_
-#define LIBTEXTCLASSIFIER_LANG_ID_CUSTOM_TOKENIZER_H_
-
-#include <cstddef>
-#include <string>
-
-#include "lang_id/light-sentence.h"
-
-namespace libtextclassifier {
-namespace nlp_core {
-namespace lang_id {
-
-// Perform custom tokenization of text. Customized for the language
-// identification project. Currently (Sep 15, 2016) we tokenize on space,
-// newline, and tab, ignore all empty tokens, and (for each of the remaining
-// tokens) prepend "^" (special token begin marker) and append "$" (special
-// token end marker).
-//
-// Tokens are stored into the words of the LightSentence *sentence.
-void TokenizeTextForLangId(const std::string &text, LightSentence *sentence);
-
-// Returns a pointer "end" inside [data, data + size) such that the prefix from
-// [data, end) is the largest one that does not contain '\0' and offers the
-// following guarantee: if one starts with
-//
-// curr = text.data()
-//
-// and keeps executing
-//
-// curr += utils::GetNumBytesForNonZeroUTF8Char(curr)
-//
-// one would eventually reach curr == end (the pointer returned by this
-// function) without accessing data outside the std::string. This guards
-// against scenarios like a broken UTF-8 string which has only e.g., the first 2
-// bytes from a 3-byte UTF8 sequence.
-const char *GetSafeEndOfString(const char *data, size_t size);
-
-static inline const char *GetSafeEndOfString(const std::string &text) {
- return GetSafeEndOfString(text.data(), text.size());
-}
-
-} // namespace lang_id
-} // namespace nlp_core
-} // namespace libtextclassifier
-
-#endif // LIBTEXTCLASSIFIER_LANG_ID_CUSTOM_TOKENIZER_H_
diff --git a/lang_id/lang-id-brain-interface.h b/lang_id/lang-id-brain-interface.h
deleted file mode 100644
index ce79497..0000000
--- a/lang_id/lang-id-brain-interface.h
+++ /dev/null
@@ -1,88 +0,0 @@
-/*
- * Copyright (C) 2017 The Android Open Source Project
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#ifndef LIBTEXTCLASSIFIER_LANG_ID_LANG_ID_BRAIN_INTERFACE_H_
-#define LIBTEXTCLASSIFIER_LANG_ID_LANG_ID_BRAIN_INTERFACE_H_
-
-#include <string>
-#include <vector>
-
-#include "common/embedding-feature-extractor.h"
-#include "common/feature-extractor.h"
-#include "common/task-context.h"
-#include "common/workspace.h"
-#include "lang_id/light-sentence-features.h"
-#include "lang_id/light-sentence.h"
-#include "util/base/macros.h"
-
-namespace libtextclassifier {
-namespace nlp_core {
-namespace lang_id {
-
-// Specialization of EmbeddingFeatureExtractor that extracts from LightSentence.
-class LangIdEmbeddingFeatureExtractor
- : public EmbeddingFeatureExtractor<LightSentenceExtractor, LightSentence> {
- public:
- LangIdEmbeddingFeatureExtractor() {}
- const std::string ArgPrefix() const override { return "language_identifier"; }
-
- TC_DISALLOW_COPY_AND_ASSIGN(LangIdEmbeddingFeatureExtractor);
-};
-
-// Handles sentence -> numeric_features and numeric_prediction -> language
-// conversions.
-class LangIdBrainInterface {
- public:
- LangIdBrainInterface() {}
-
- // Initializes resources and parameters.
- bool Init(TaskContext *context) {
- if (!feature_extractor_.Init(context)) {
- return false;
- }
- feature_extractor_.RequestWorkspaces(&workspace_registry_);
- return true;
- }
-
- // Extract features from sentence. On return, FeatureVector features[i]
- // contains the features for the embedding space #i.
- void GetFeatures(LightSentence *sentence,
- std::vector<FeatureVector> *features) const {
- WorkspaceSet workspace;
- workspace.Reset(workspace_registry_);
- feature_extractor_.Preprocess(&workspace, sentence);
- return feature_extractor_.ExtractFeatures(workspace, *sentence, features);
- }
-
- int NumEmbeddings() const {
- return feature_extractor_.NumEmbeddings();
- }
-
- private:
- // Typed feature extractor for embeddings.
- LangIdEmbeddingFeatureExtractor feature_extractor_;
-
- // The registry of shared workspaces in the feature extractor.
- WorkspaceRegistry workspace_registry_;
-
- TC_DISALLOW_COPY_AND_ASSIGN(LangIdBrainInterface);
-};
-
-} // namespace lang_id
-} // namespace nlp_core
-} // namespace libtextclassifier
-
-#endif // LIBTEXTCLASSIFIER_LANG_ID_LANG_ID_BRAIN_INTERFACE_H_
diff --git a/lang_id/lang-id.cc b/lang_id/lang-id.cc
deleted file mode 100644
index 8383d33..0000000
--- a/lang_id/lang-id.cc
+++ /dev/null
@@ -1,402 +0,0 @@
-/*
- * Copyright (C) 2017 The Android Open Source Project
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#include "lang_id/lang-id.h"
-
-#include <stdio.h>
-
-#include <algorithm>
-#include <limits>
-#include <memory>
-#include <string>
-#include <vector>
-
-#include "common/algorithm.h"
-#include "common/embedding-network-params-from-proto.h"
-#include "common/embedding-network.pb.h"
-#include "common/embedding-network.h"
-#include "common/feature-extractor.h"
-#include "common/file-utils.h"
-#include "common/list-of-strings.pb.h"
-#include "common/memory_image/in-memory-model-data.h"
-#include "common/mmap.h"
-#include "common/softmax.h"
-#include "common/task-context.h"
-#include "lang_id/custom-tokenizer.h"
-#include "lang_id/lang-id-brain-interface.h"
-#include "lang_id/language-identifier-features.h"
-#include "lang_id/light-sentence-features.h"
-#include "lang_id/light-sentence.h"
-#include "lang_id/relevant-script-feature.h"
-#include "util/base/logging.h"
-#include "util/base/macros.h"
-
-using ::libtextclassifier::nlp_core::file_utils::ParseProtoFromMemory;
-
-namespace libtextclassifier {
-namespace nlp_core {
-namespace lang_id {
-
-namespace {
-// Default value for the probability threshold; see comments for
-// LangId::SetProbabilityThreshold().
-static const float kDefaultProbabilityThreshold = 0.50;
-
-// Default value for min text size below which our model can't provide a
-// meaningful prediction.
-static const int kDefaultMinTextSizeInBytes = 20;
-
-// Initial value for the default language for LangId::FindLanguage(). The
-// default language can be changed (for an individual LangId object) using
-// LangId::SetDefaultLanguage().
-static const char kInitialDefaultLanguage[] = "";
-
-// Returns total number of bytes of the words from sentence, without the ^
-// (start-of-word) and $ (end-of-word) markers. Note: "real text" means that
-// this ignores whitespace and punctuation characters from the original text.
-int GetRealTextSize(const LightSentence &sentence) {
- int total = 0;
- for (int i = 0; i < sentence.num_words(); ++i) {
- TC_DCHECK(!sentence.word(i).empty());
- TC_DCHECK_EQ('^', sentence.word(i).front());
- TC_DCHECK_EQ('$', sentence.word(i).back());
- total += sentence.word(i).size() - 2;
- }
- return total;
-}
-
-} // namespace
-
-// Class that performs all work behind LangId.
-class LangIdImpl {
- public:
- explicit LangIdImpl(const std::string &filename) {
- // Using mmap as a fast way to read the model bytes.
- ScopedMmap scoped_mmap(filename);
- MmapHandle mmap_handle = scoped_mmap.handle();
- if (!mmap_handle.ok()) {
- TC_LOG(ERROR) << "Unable to read model bytes.";
- return;
- }
-
- Initialize(mmap_handle.to_stringpiece());
- }
-
- explicit LangIdImpl(int fd) {
- // Using mmap as a fast way to read the model bytes.
- ScopedMmap scoped_mmap(fd);
- MmapHandle mmap_handle = scoped_mmap.handle();
- if (!mmap_handle.ok()) {
- TC_LOG(ERROR) << "Unable to read model bytes.";
- return;
- }
-
- Initialize(mmap_handle.to_stringpiece());
- }
-
- LangIdImpl(const char *ptr, size_t length) {
- Initialize(StringPiece(ptr, length));
- }
-
- void Initialize(StringPiece model_bytes) {
- // Will set valid_ to true only on successful initialization.
- valid_ = false;
-
- // Make sure all relevant features are registered:
- ContinuousBagOfNgramsFunction::RegisterClass();
- RelevantScriptFeature::RegisterClass();
-
- // NOTE(salcianu): code below relies on the fact that the current features
- // do not rely on data from a TaskInput. Otherwise, one would have to use
- // the more complex model registration mechanism, which requires more code.
- InMemoryModelData model_data(model_bytes);
- TaskContext context;
- if (!model_data.GetTaskSpec(context.mutable_spec())) {
- TC_LOG(ERROR) << "Unable to get model TaskSpec";
- return;
- }
-
- if (!ParseNetworkParams(model_data, &context)) {
- return;
- }
- if (!ParseListOfKnownLanguages(model_data, &context)) {
- return;
- }
-
- network_.reset(new EmbeddingNetwork(network_params_.get()));
- if (!network_->is_valid()) {
- return;
- }
-
- probability_threshold_ =
- context.Get("reliability_thresh", kDefaultProbabilityThreshold);
- min_text_size_in_bytes_ =
- context.Get("min_text_size_in_bytes", kDefaultMinTextSizeInBytes);
- version_ = context.Get("version", 0);
-
- if (!lang_id_brain_interface_.Init(&context)) {
- return;
- }
- valid_ = true;
- }
-
- void SetProbabilityThreshold(float threshold) {
- probability_threshold_ = threshold;
- }
-
- void SetDefaultLanguage(const std::string &lang) { default_language_ = lang; }
-
- std::string FindLanguage(const std::string &text) const {
- std::vector<float> scores = ScoreLanguages(text);
- if (scores.empty()) {
- return default_language_;
- }
-
- // Softmax label with max score.
- int label = GetArgMax(scores);
- float probability = scores[label];
- if (probability < probability_threshold_) {
- return default_language_;
- }
- return GetLanguageForSoftmaxLabel(label);
- }
-
- std::vector<std::pair<std::string, float>> FindLanguages(
- const std::string &text) const {
- std::vector<float> scores = ScoreLanguages(text);
-
- std::vector<std::pair<std::string, float>> result;
- for (int i = 0; i < scores.size(); i++) {
- result.push_back({GetLanguageForSoftmaxLabel(i), scores[i]});
- }
-
- // To avoid crashing clients that always expect at least one predicted
- // language, we promised (see doc for this method) that the result always
- // contains at least one element.
- if (result.empty()) {
- // We use a tiny probability, such that any client that uses a meaningful
- // probability threshold ignores this prediction. We don't use 0.0f, to
- // avoid crashing clients that normalize the probabilities we return here.
- result.push_back({default_language_, 0.001f});
- }
- return result;
- }
-
- std::vector<float> ScoreLanguages(const std::string &text) const {
- if (!is_valid()) {
- return {};
- }
-
- // Create a Sentence storing the input text.
- LightSentence sentence;
- TokenizeTextForLangId(text, &sentence);
-
- if (GetRealTextSize(sentence) < min_text_size_in_bytes_) {
- return {};
- }
-
- // TODO(salcianu): reuse vector<FeatureVector>.
- std::vector<FeatureVector> features(
- lang_id_brain_interface_.NumEmbeddings());
- lang_id_brain_interface_.GetFeatures(&sentence, &features);
-
- // Predict language.
- EmbeddingNetwork::Vector scores;
- network_->ComputeFinalScores(features, &scores);
-
- return ComputeSoftmax(scores);
- }
-
- bool is_valid() const { return valid_; }
-
- int version() const { return version_; }
-
- private:
- // Returns name of the (in-memory) file for the indicated TaskInput from
- // context.
- static std::string GetInMemoryFileNameForTaskInput(
- const std::string &input_name, TaskContext *context) {
- TaskInput *task_input = context->GetInput(input_name);
- if (task_input->part_size() != 1) {
- TC_LOG(ERROR) << "TaskInput " << input_name << " has "
- << task_input->part_size() << " parts";
- return "";
- }
- return task_input->part(0).file_pattern();
- }
-
- bool ParseNetworkParams(const InMemoryModelData &model_data,
- TaskContext *context) {
- const std::string input_name = "language-identifier-network";
- const std::string input_file_name =
- GetInMemoryFileNameForTaskInput(input_name, context);
- if (input_file_name.empty()) {
- TC_LOG(ERROR) << "No input file name for TaskInput " << input_name;
- return false;
- }
- StringPiece bytes = model_data.GetBytesForInputFile(input_file_name);
- if (bytes.data() == nullptr) {
- TC_LOG(ERROR) << "Unable to get bytes for TaskInput " << input_name;
- return false;
- }
- std::unique_ptr<EmbeddingNetworkProto> proto(new EmbeddingNetworkProto());
- if (!ParseProtoFromMemory(bytes, proto.get())) {
- TC_LOG(ERROR) << "Unable to parse EmbeddingNetworkProto";
- return false;
- }
- network_params_.reset(
- new EmbeddingNetworkParamsFromProto(std::move(proto)));
- if (!network_params_->is_valid()) {
- TC_LOG(ERROR) << "EmbeddingNetworkParamsFromProto not valid";
- return false;
- }
- return true;
- }
-
- // Parses dictionary with known languages (i.e., field languages_) from a
- // TaskInput of context. Note: that TaskInput should be a ListOfStrings proto
- // with a single element, the serialized form of a ListOfStrings.
- //
- bool ParseListOfKnownLanguages(const InMemoryModelData &model_data,
- TaskContext *context) {
- const std::string input_name = "language-name-id-map";
- const std::string input_file_name =
- GetInMemoryFileNameForTaskInput(input_name, context);
- if (input_file_name.empty()) {
- TC_LOG(ERROR) << "No input file name for TaskInput " << input_name;
- return false;
- }
- StringPiece bytes = model_data.GetBytesForInputFile(input_file_name);
- if (bytes.data() == nullptr) {
- TC_LOG(ERROR) << "Unable to get bytes for TaskInput " << input_name;
- return false;
- }
- ListOfStrings records;
- if (!ParseProtoFromMemory(bytes, &records)) {
- TC_LOG(ERROR) << "Unable to parse ListOfStrings from TaskInput "
- << input_name;
- return false;
- }
- if (records.element_size() != 1) {
- TC_LOG(ERROR) << "Wrong number of records in TaskInput " << input_name
- << " : " << records.element_size();
- return false;
- }
- if (!ParseProtoFromMemory(std::string(records.element(0)), &languages_)) {
- TC_LOG(ERROR) << "Unable to parse dictionary with known languages";
- return false;
- }
- return true;
- }
-
- // Returns language code for a softmax label. See comments for languages_
- // field. If label is out of range, returns default_language_.
- std::string GetLanguageForSoftmaxLabel(int label) const {
- if ((label >= 0) && (label < languages_.element_size())) {
- return languages_.element(label);
- } else {
- TC_LOG(ERROR) << "Softmax label " << label << " outside range [0, "
- << languages_.element_size() << ")";
- return default_language_;
- }
- }
-
- LangIdBrainInterface lang_id_brain_interface_;
-
- // Parameters for the neural network network_ (see below).
- std::unique_ptr<EmbeddingNetworkParamsFromProto> network_params_;
-
- // Neural network to use for scoring.
- std::unique_ptr<EmbeddingNetwork> network_;
-
- // True if this object is ready to perform language predictions.
- bool valid_;
-
- // Only predictions with a probability (confidence) above this threshold are
- // reported. Otherwise, we report default_language_.
- float probability_threshold_ = kDefaultProbabilityThreshold;
-
- // Min size of the input text for our predictions to be meaningful. Below
- // this threshold, the underlying model may report a wrong language and a high
- // confidence score.
- int min_text_size_in_bytes_ = kDefaultMinTextSizeInBytes;
-
- // Version of the model.
- int version_ = -1;
-
- // Known languages: softmax label i (an integer) means languages_.element(i)
- // (something like "en", "fr", "ru", etc).
- ListOfStrings languages_;
-
- // Language code to return in case of errors.
- std::string default_language_ = kInitialDefaultLanguage;
-
- TC_DISALLOW_COPY_AND_ASSIGN(LangIdImpl);
-};
-
-LangId::LangId(const std::string &filename) : pimpl_(new LangIdImpl(filename)) {
- if (!pimpl_->is_valid()) {
- TC_LOG(ERROR) << "Unable to construct a valid LangId based "
- << "on the data from " << filename
- << "; nothing should crash, but "
- << "accuracy will be bad.";
- }
-}
-
-LangId::LangId(int fd) : pimpl_(new LangIdImpl(fd)) {
- if (!pimpl_->is_valid()) {
- TC_LOG(ERROR) << "Unable to construct a valid LangId based "
- << "on the data from descriptor " << fd
- << "; nothing should crash, "
- << "but accuracy will be bad.";
- }
-}
-
-LangId::LangId(const char *ptr, size_t length)
- : pimpl_(new LangIdImpl(ptr, length)) {
- if (!pimpl_->is_valid()) {
- TC_LOG(ERROR) << "Unable to construct a valid LangId based "
- << "on the memory region; nothing should crash, "
- << "but accuracy will be bad.";
- }
-}
-
-LangId::~LangId() = default;
-
-void LangId::SetProbabilityThreshold(float threshold) {
- pimpl_->SetProbabilityThreshold(threshold);
-}
-
-void LangId::SetDefaultLanguage(const std::string &lang) {
- pimpl_->SetDefaultLanguage(lang);
-}
-
-std::string LangId::FindLanguage(const std::string &text) const {
- return pimpl_->FindLanguage(text);
-}
-
-std::vector<std::pair<std::string, float>> LangId::FindLanguages(
- const std::string &text) const {
- return pimpl_->FindLanguages(text);
-}
-
-bool LangId::is_valid() const { return pimpl_->is_valid(); }
-
-int LangId::version() const { return pimpl_->version(); }
-
-} // namespace lang_id
-} // namespace nlp_core
-} // namespace libtextclassifier
diff --git a/lang_id/lang-id.h b/lang_id/lang-id.h
deleted file mode 100644
index 7653dde..0000000
--- a/lang_id/lang-id.h
+++ /dev/null
@@ -1,112 +0,0 @@
-/*
- * Copyright (C) 2017 The Android Open Source Project
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#ifndef LIBTEXTCLASSIFIER_LANG_ID_LANG_ID_H_
-#define LIBTEXTCLASSIFIER_LANG_ID_LANG_ID_H_
-
-// Clients who want to perform language identification should use this header.
-//
-// Note for lang id implementors: keep this header as linght as possible. E.g.,
-// any macro defined here (or in a transitively #included file) is a potential
-// name conflict with our clients.
-
-#include <memory>
-#include <string>
-#include <vector>
-
-#include "util/base/macros.h"
-
-namespace libtextclassifier {
-namespace nlp_core {
-namespace lang_id {
-
-// Forward-declaration of the class that performs all underlying work.
-class LangIdImpl;
-
-// Class for detecting the language of a document.
-//
-// NOTE: this class is thread-unsafe.
-class LangId {
- public:
- // Constructs a LangId object, loading an EmbeddingNetworkProto model from the
- // indicated file.
- //
- // Note: we don't crash if we detect a problem at construction time (e.g.,
- // file doesn't exist, or its content is corrupted). Instead, we mark the
- // newly-constructed object as invalid; clients can invoke FindLanguage() on
- // an invalid object: nothing crashes, but accuracy will be bad.
- explicit LangId(const std::string &filename);
-
- // Same as above but uses a file descriptor.
- explicit LangId(int fd);
-
- // Same as above but uses already mapped memory region
- explicit LangId(const char *ptr, size_t length);
-
- virtual ~LangId();
-
- // Sets probability threshold for predictions. If our likeliest prediction is
- // below this threshold, we report the default language (see
- // SetDefaultLanguage()). Othewise, we report the likelist language.
- //
- // By default (if this method is not called) we use the probability threshold
- // stored in the model, as the task parameter "reliability_thresh". If that
- // task parameter is not specified, we use 0.5. A client can use this method
- // to get a different precision / recall trade-off. The higher the threshold,
- // the higher the precision and lower the recall rate.
- void SetProbabilityThreshold(float threshold);
-
- // Sets default language to report if errors prevent running the real
- // inference code or if prediction confidence is too small.
- void SetDefaultLanguage(const std::string &lang);
-
- // Returns language code for the most likely language that text is written in.
- // Note: if this LangId object is not valid (see
- // is_valid()), this method returns the default language specified via
- // SetDefaultLanguage() or (if that method was never invoked), the empty
- // std::string.
- std::string FindLanguage(const std::string &text) const;
-
- // Returns a vector of language codes along with the probability for each
- // language. The result contains at least one element. The sum of
- // probabilities may be less than 1.0.
- std::vector<std::pair<std::string, float>> FindLanguages(
- const std::string &text) const;
-
- // Returns true if this object has been correctly initialized and is ready to
- // perform predictions. For more info, see doc for LangId
- // constructor above.
- bool is_valid() const;
-
- // Returns version number for the model.
- int version() const;
-
- private:
- // Returns a vector of probabilities of languages of the text.
- std::vector<float> ScoreLanguages(const std::string &text) const;
-
- // Pimpl ("pointer to implementation") pattern, to hide all internals from our
- // clients.
- std::unique_ptr<LangIdImpl> pimpl_;
-
- TC_DISALLOW_COPY_AND_ASSIGN(LangId);
-};
-
-} // namespace lang_id
-} // namespace nlp_core
-} // namespace libtextclassifier
-
-#endif // LIBTEXTCLASSIFIER_LANG_ID_LANG_ID_H_
diff --git a/lang_id/lang-id_test.cc b/lang_id/lang-id_test.cc
deleted file mode 100644
index 2f8aedd..0000000
--- a/lang_id/lang-id_test.cc
+++ /dev/null
@@ -1,152 +0,0 @@
-/*
- * Copyright (C) 2017 The Android Open Source Project
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#include "lang_id/lang-id.h"
-
-#include <memory>
-#include <string>
-#include <utility>
-#include <vector>
-
-#include "util/base/logging.h"
-#include "gtest/gtest.h"
-
-namespace libtextclassifier {
-namespace nlp_core {
-namespace lang_id {
-
-namespace {
-
-std::string GetModelPath() {
- return TEST_DATA_DIR "langid.model";
-}
-
-// Creates a LangId with default model. Passes ownership to
-// the caller.
-LangId *CreateLanguageDetector() { return new LangId(GetModelPath()); }
-
-} // namespace
-
-TEST(LangIdTest, Normal) {
- std::unique_ptr<LangId> lang_id(CreateLanguageDetector());
-
- EXPECT_EQ("en", lang_id->FindLanguage("This text is written in English."));
- EXPECT_EQ("en",
- lang_id->FindLanguage("This text is written in English. "));
- EXPECT_EQ("en",
- lang_id->FindLanguage(" This text is written in English. "));
- EXPECT_EQ("fr", lang_id->FindLanguage("Vive la France! Vive la France!"));
- EXPECT_EQ("ro", lang_id->FindLanguage("Sunt foarte foarte foarte fericit!"));
-}
-
-// Test that for very small queries, we return the default language and a low
-// confidence score.
-TEST(LangIdTest, SuperSmallQueries) {
- std::unique_ptr<LangId> lang_id(CreateLanguageDetector());
-
- // Use a default language different from any real language: to be sure the
- // result is the default language, not a language that happens to be the
- // default language.
- const std::string kDefaultLanguage = "dflt-lng";
- lang_id->SetDefaultLanguage(kDefaultLanguage);
-
- // Test the simple FindLanguage() method: that method returns a single
- // language.
- EXPECT_EQ(kDefaultLanguage, lang_id->FindLanguage("y"));
- EXPECT_EQ(kDefaultLanguage, lang_id->FindLanguage("j"));
- EXPECT_EQ(kDefaultLanguage, lang_id->FindLanguage("l"));
- EXPECT_EQ(kDefaultLanguage, lang_id->FindLanguage("w"));
- EXPECT_EQ(kDefaultLanguage, lang_id->FindLanguage("z"));
- EXPECT_EQ(kDefaultLanguage, lang_id->FindLanguage("zulu"));
-
- // Test the more complex FindLanguages() method: that method returns a vector
- // of (language, confidence_score) pairs.
- std::vector<std::pair<std::string, float>> languages;
- languages = lang_id->FindLanguages("y");
- EXPECT_EQ(1, languages.size());
- EXPECT_EQ(kDefaultLanguage, languages[0].first);
- EXPECT_GT(0.01f, languages[0].second);
-
- languages = lang_id->FindLanguages("Todoist");
- EXPECT_EQ(1, languages.size());
- EXPECT_EQ(kDefaultLanguage, languages[0].first);
- EXPECT_GT(0.01f, languages[0].second);
-
- // A few tests with a default language that is a real language code.
- const std::string kJapanese = "ja";
- lang_id->SetDefaultLanguage(kJapanese);
- EXPECT_EQ(kJapanese, lang_id->FindLanguage("y"));
- EXPECT_EQ(kJapanese, lang_id->FindLanguage("j"));
- EXPECT_EQ(kJapanese, lang_id->FindLanguage("l"));
- languages = lang_id->FindLanguages("y");
- EXPECT_EQ(1, languages.size());
- EXPECT_EQ(kJapanese, languages[0].first);
- EXPECT_GT(0.01f, languages[0].second);
-
- // Make sure the min text size limit is applied to the number of real
- // characters (e.g., without spaces and punctuation chars, which don't
- // influence language identification).
- const std::string kWhitespaces = " \t \n \t\t\t\n \t";
- const std::string kPunctuation = "... ?!!--- -%%^...-";
- std::string still_small_string = kWhitespaces + "y" + kWhitespaces +
- kPunctuation + kWhitespaces + kPunctuation +
- kPunctuation;
- EXPECT_LE(100, still_small_string.size());
- lang_id->SetDefaultLanguage(kDefaultLanguage);
- EXPECT_EQ(kDefaultLanguage, lang_id->FindLanguage(still_small_string));
- languages = lang_id->FindLanguages(still_small_string);
- EXPECT_EQ(1, languages.size());
- EXPECT_EQ(kDefaultLanguage, languages[0].first);
- EXPECT_GT(0.01f, languages[0].second);
-}
-
-namespace {
-void CheckPredictionForGibberishStrings(const std::string &default_language) {
- static const char *const kGibberish[] = {
- "",
- " ",
- " ",
- " ___ ",
- "123 456 789",
- "><> (-_-) <><",
- nullptr,
- };
-
- std::unique_ptr<LangId> lang_id(CreateLanguageDetector());
- TC_LOG(INFO) << "Default language: " << default_language;
- lang_id->SetDefaultLanguage(default_language);
- for (int i = 0; true; ++i) {
- const char *gibberish = kGibberish[i];
- if (gibberish == nullptr) {
- break;
- }
- const std::string predicted_language = lang_id->FindLanguage(gibberish);
- TC_LOG(INFO) << "Predicted " << predicted_language << " for \"" << gibberish
- << "\"";
- EXPECT_EQ(default_language, predicted_language);
- }
-}
-} // namespace
-
-TEST(LangIdTest, CornerCases) {
- CheckPredictionForGibberishStrings("en");
- CheckPredictionForGibberishStrings("ro");
- CheckPredictionForGibberishStrings("fr");
-}
-
-} // namespace lang_id
-} // namespace nlp_core
-} // namespace libtextclassifier
diff --git a/lang_id/language-identifier-features.cc b/lang_id/language-identifier-features.cc
deleted file mode 100644
index 2e3912e..0000000
--- a/lang_id/language-identifier-features.cc
+++ /dev/null
@@ -1,141 +0,0 @@
-/*
- * Copyright (C) 2017 The Android Open Source Project
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#include "lang_id/language-identifier-features.h"
-
-#include <utility>
-#include <vector>
-
-#include "common/feature-extractor.h"
-#include "common/feature-types.h"
-#include "common/task-context.h"
-#include "util/hash/hash.h"
-#include "util/strings/utf8.h"
-
-namespace libtextclassifier {
-namespace nlp_core {
-namespace lang_id {
-
-bool ContinuousBagOfNgramsFunction::Setup(TaskContext *context) {
- // Parameters in the feature function descriptor.
- ngram_id_dimension_ = GetIntParameter("id_dim", 10000);
- ngram_size_ = GetIntParameter("size", 3);
-
- counts_.assign(ngram_id_dimension_, 0);
- return true;
-}
-
-bool ContinuousBagOfNgramsFunction::Init(TaskContext *context) {
- set_feature_type(new NumericFeatureType(name(), ngram_id_dimension_));
- return true;
-}
-
-int ContinuousBagOfNgramsFunction::ComputeNgramCounts(
- const LightSentence &sentence) const {
- // Invariant 1: counts_.size() == ngram_id_dimension_. Holds at the end of
- // the constructor. After that, no method changes counts_.size().
- TC_DCHECK_EQ(counts_.size(), ngram_id_dimension_);
-
- // Invariant 2: the vector non_zero_count_indices_ is empty. The vector
- // non_zero_count_indices_ is empty at construction time and gets emptied at
- // the end of each call to Evaluate(). Hence, this invariant holds at the
- // beginning of each run of Evaluate(), where the only call to this code takes
- // place.
- TC_DCHECK(non_zero_count_indices_.empty());
-
- int total_count = 0;
-
- for (int i = 0; i < sentence.num_words(); ++i) {
- const std::string &word = sentence.word(i);
- const char *const word_end = word.data() + word.size();
-
- // Set ngram_start at the start of the current token (word).
- const char *ngram_start = word.data();
-
- // Set ngram_end ngram_size UTF8 characters after ngram_start. Note: each
- // UTF8 character contains between 1 and 4 bytes.
- const char *ngram_end = ngram_start;
- int num_utf8_chars = 0;
- do {
- ngram_end += GetNumBytesForNonZeroUTF8Char(ngram_end);
- num_utf8_chars++;
- } while ((num_utf8_chars < ngram_size_) && (ngram_end < word_end));
-
- if (num_utf8_chars < ngram_size_) {
- // Current token is so small, it does not contain a single ngram of
- // ngram_size UTF8 characters. Not much we can do in this case ...
- continue;
- }
-
- // At this point, [ngram_start, ngram_end) is the first ngram of ngram_size
- // UTF8 characters from current token.
- while (true) {
- // Compute ngram_id: hash(ngram) % ngram_id_dimension
- int ngram_id =
- (Hash32WithDefaultSeed(ngram_start, ngram_end - ngram_start) %
- ngram_id_dimension_);
-
- // Use a reference to the actual count, such that we can both test whether
- // the count was 0 and increment it without perfoming two lookups.
- //
- // Due to the way we compute ngram_id, 0 <= ngram_id < ngram_id_dimension.
- // Hence, by Invariant 1 (above), the access counts_[ngram_id] is safe.
- int &ref_to_count_for_ngram = counts_[ngram_id];
- if (ref_to_count_for_ngram == 0) {
- non_zero_count_indices_.push_back(ngram_id);
- }
- ref_to_count_for_ngram++;
- total_count++;
- if (ngram_end >= word_end) {
- break;
- }
-
- // Advance both ngram_start and ngram_end by one UTF8 character. This
- // way, the number of UTF8 characters between them remains constant
- // (ngram_size).
- ngram_start += GetNumBytesForNonZeroUTF8Char(ngram_start);
- ngram_end += GetNumBytesForNonZeroUTF8Char(ngram_end);
- }
- } // end of loop over tokens.
-
- return total_count;
-}
-
-void ContinuousBagOfNgramsFunction::Evaluate(const WorkspaceSet &workspaces,
- const LightSentence &sentence,
- FeatureVector *result) const {
- // Find the char ngram counts.
- int total_count = ComputeNgramCounts(sentence);
-
- // Populate the feature vector.
- const float norm = static_cast<float>(total_count);
-
- for (int ngram_id : non_zero_count_indices_) {
- const float weight = counts_[ngram_id] / norm;
- FloatFeatureValue value(ngram_id, weight);
- result->add(feature_type(), value.discrete_value);
-
- // Clear up counts_, for the next invocation of Evaluate().
- counts_[ngram_id] = 0;
- }
-
- // Clear up non_zero_count_indices_, for the next invocation of Evaluate().
- non_zero_count_indices_.clear();
-}
-
-} // namespace lang_id
-} // namespace nlp_core
-} // namespace libtextclassifier
diff --git a/lang_id/language-identifier-features.h b/lang_id/language-identifier-features.h
deleted file mode 100644
index a4e3b3d..0000000
--- a/lang_id/language-identifier-features.h
+++ /dev/null
@@ -1,79 +0,0 @@
-/*
- * Copyright (C) 2017 The Android Open Source Project
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#ifndef LIBTEXTCLASSIFIER_LANG_ID_LANGUAGE_IDENTIFIER_FEATURES_H_
-#define LIBTEXTCLASSIFIER_LANG_ID_LANGUAGE_IDENTIFIER_FEATURES_H_
-
-#include <string>
-
-#include "common/feature-extractor.h"
-#include "common/task-context.h"
-#include "common/workspace.h"
-#include "lang_id/light-sentence-features.h"
-#include "lang_id/light-sentence.h"
-
-namespace libtextclassifier {
-namespace nlp_core {
-namespace lang_id {
-
-// Class for computing continuous char ngram features.
-//
-// Feature function descriptor parameters:
-// id_dim(int, 10000):
-// The integer id of each char ngram is computed as follows:
-// Hash32WithDefaultSeed(char ngram) % id_dim.
-// size(int, 3):
-// Only ngrams of this size will be extracted.
-//
-// NOTE: this class is not thread-safe. TODO(salcianu): make it thread-safe.
-class ContinuousBagOfNgramsFunction : public LightSentenceFeature {
- public:
- bool Setup(TaskContext *context) override;
- bool Init(TaskContext *context) override;
-
- // Appends the features computed from the sentence to the feature vector.
- void Evaluate(const WorkspaceSet &workspaces, const LightSentence &sentence,
- FeatureVector *result) const override;
-
- TC_DEFINE_REGISTRATION_METHOD("continuous-bag-of-ngrams",
- ContinuousBagOfNgramsFunction);
-
- private:
- // Auxiliary for Evaluate(). Fills counts_ and non_zero_count_indices_ (see
- // below), and returns the total ngram count.
- int ComputeNgramCounts(const LightSentence &sentence) const;
-
- // counts_[i] is the count of all ngrams with id i. Work data for Evaluate().
- // NOTE: we declare this vector as a field, such that its underlying capacity
- // stays allocated in between calls to Evaluate().
- mutable std::vector<int> counts_;
-
- // Indices of non-zero elements of counts_. See comments for counts_.
- mutable std::vector<int> non_zero_count_indices_;
-
- // The integer id of each char ngram is computed as follows:
- // Hash32WithDefaultSeed(char_ngram) % ngram_id_dimension_.
- int ngram_id_dimension_;
-
- // Only ngrams of size ngram_size_ will be extracted.
- int ngram_size_;
-};
-
-} // namespace lang_id
-} // namespace nlp_core
-} // namespace libtextclassifier
-
-#endif // LIBTEXTCLASSIFIER_LANG_ID_LANGUAGE_IDENTIFIER_FEATURES_H_
diff --git a/lang_id/light-sentence-features.cc b/lang_id/light-sentence-features.cc
deleted file mode 100644
index aec6b81..0000000
--- a/lang_id/light-sentence-features.cc
+++ /dev/null
@@ -1,29 +0,0 @@
-/*
- * Copyright (C) 2017 The Android Open Source Project
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#include "lang_id/light-sentence-features.h"
-
-#include "common/registry.h"
-
-namespace libtextclassifier {
-namespace nlp_core {
-
-// Registry for the features on whole light sentences.
-TC_DEFINE_CLASS_REGISTRY_NAME("light sentence feature function",
- lang_id::LightSentenceFeature);
-
-} // namespace nlp_core
-} // namespace libtextclassifier
diff --git a/lang_id/light-sentence-features.h b/lang_id/light-sentence-features.h
deleted file mode 100644
index a140f65..0000000
--- a/lang_id/light-sentence-features.h
+++ /dev/null
@@ -1,41 +0,0 @@
-/*
- * Copyright (C) 2017 The Android Open Source Project
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#ifndef LIBTEXTCLASSIFIER_LANG_ID_LIGHT_SENTENCE_FEATURES_H_
-#define LIBTEXTCLASSIFIER_LANG_ID_LIGHT_SENTENCE_FEATURES_H_
-
-#include "common/feature-extractor.h"
-#include "lang_id/light-sentence.h"
-
-namespace libtextclassifier {
-namespace nlp_core {
-namespace lang_id {
-
-// Feature function that extracts features from LightSentences.
-typedef FeatureFunction<LightSentence> LightSentenceFeature;
-
-// Feature extractor for LightSentences.
-typedef FeatureExtractor<LightSentence> LightSentenceExtractor;
-
-} // namespace lang_id
-
-// Should be used in namespace libtextclassifier::nlp_core.
-TC_DECLARE_CLASS_REGISTRY_NAME(lang_id::LightSentenceFeature);
-
-} // namespace nlp_core
-} // namespace libtextclassifier
-
-#endif // LIBTEXTCLASSIFIER_LANG_ID_LIGHT_SENTENCE_FEATURES_H_
diff --git a/lang_id/light-sentence.h b/lang_id/light-sentence.h
deleted file mode 100644
index e8451be..0000000
--- a/lang_id/light-sentence.h
+++ /dev/null
@@ -1,66 +0,0 @@
-/*
- * Copyright (C) 2017 The Android Open Source Project
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#ifndef LIBTEXTCLASSIFIER_LANG_ID_LIGHT_SENTENCE_H_
-#define LIBTEXTCLASSIFIER_LANG_ID_LIGHT_SENTENCE_H_
-
-#include <string>
-#include <vector>
-
-#include "util/base/logging.h"
-#include "util/base/macros.h"
-
-namespace libtextclassifier {
-namespace nlp_core {
-namespace lang_id {
-
-// Simplified replacement for the Sentence proto, for internal use in the
-// language identification code.
-//
-// In this simplified form, a sentence is a vector of words, each word being a
-// string.
-class LightSentence {
- public:
- LightSentence() {}
-
- // Adds a new word after all existing ones, and returns a pointer to it. The
- // new word is initialized to the empty string.
- std::string *add_word() {
- words_.emplace_back();
- return &(words_.back());
- }
-
- // Returns number of words from this LightSentence.
- int num_words() const { return words_.size(); }
-
- // Returns the ith word from this LightSentence. Note: undefined behavior if
- // i is out of bounds.
- const std::string &word(int i) const {
- TC_DCHECK((i >= 0) && (i < num_words()));
- return words_[i];
- }
-
- private:
- std::vector<std::string> words_;
-
- TC_DISALLOW_COPY_AND_ASSIGN(LightSentence);
-};
-
-} // namespace lang_id
-} // namespace nlp_core
-} // namespace libtextclassifier
-
-#endif // LIBTEXTCLASSIFIER_LANG_ID_LIGHT_SENTENCE_H_
diff --git a/lang_id/relevant-script-feature.cc b/lang_id/relevant-script-feature.cc
deleted file mode 100644
index c865ce5..0000000
--- a/lang_id/relevant-script-feature.cc
+++ /dev/null
@@ -1,91 +0,0 @@
-/*
- * Copyright (C) 2017 The Android Open Source Project
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#include "lang_id/relevant-script-feature.h"
-
-#include <string>
-
-#include "common/feature-extractor.h"
-#include "common/feature-types.h"
-#include "common/task-context.h"
-#include "common/workspace.h"
-#include "lang_id/script-detector.h"
-#include "util/base/logging.h"
-#include "util/strings/utf8.h"
-
-namespace libtextclassifier {
-namespace nlp_core {
-namespace lang_id {
-
-bool RelevantScriptFeature::Setup(TaskContext *context) { return true; }
-
-bool RelevantScriptFeature::Init(TaskContext *context) {
- set_feature_type(new NumericFeatureType(name(), kNumRelevantScripts));
- return true;
-}
-
-void RelevantScriptFeature::Evaluate(const WorkspaceSet &workspaces,
- const LightSentence &sentence,
- FeatureVector *result) const {
- // We expect kNumRelevantScripts to be small, so we stack-allocate the array
- // of counts. Still, if that changes, we want to find out.
- static_assert(
- kNumRelevantScripts < 25,
- "switch counts to vector<int>: too big for stack-allocated int[]");
-
- // counts[s] is the number of characters with script s.
- // Note: {} "value-initializes" the array to zero.
- int counts[kNumRelevantScripts]{};
- int total_count = 0;
- for (int i = 0; i < sentence.num_words(); ++i) {
- const std::string &word = sentence.word(i);
- const char *const word_end = word.data() + word.size();
- const char *curr = word.data();
-
- // Skip over token start '^'.
- TC_DCHECK_EQ(*curr, '^');
- curr += GetNumBytesForNonZeroUTF8Char(curr);
- while (true) {
- const int num_bytes = GetNumBytesForNonZeroUTF8Char(curr);
- Script script = GetScript(curr, num_bytes);
-
- // We do this update and the if (...) break below *before* incrementing
- // counts[script] in order to skip the token end '$'.
- curr += num_bytes;
- if (curr >= word_end) {
- TC_DCHECK_EQ(*(curr - num_bytes), '$');
- break;
- }
- TC_DCHECK_GE(script, 0);
- TC_DCHECK_LT(script, kNumRelevantScripts);
- counts[script]++;
- total_count++;
- }
- }
-
- for (int script_id = 0; script_id < kNumRelevantScripts; ++script_id) {
- int count = counts[script_id];
- if (count > 0) {
- const float weight = static_cast<float>(count) / total_count;
- FloatFeatureValue value(script_id, weight);
- result->add(feature_type(), value.discrete_value);
- }
- }
-}
-
-} // namespace lang_id
-} // namespace nlp_core
-} // namespace libtextclassifier
diff --git a/lang_id/relevant-script-feature.h b/lang_id/relevant-script-feature.h
deleted file mode 100644
index 2aa2420..0000000
--- a/lang_id/relevant-script-feature.h
+++ /dev/null
@@ -1,58 +0,0 @@
-/*
- * Copyright (C) 2017 The Android Open Source Project
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#ifndef LIBTEXTCLASSIFIER_LANG_ID_RELEVANT_SCRIPT_FEATURE_H_
-#define LIBTEXTCLASSIFIER_LANG_ID_RELEVANT_SCRIPT_FEATURE_H_
-
-#include "common/feature-extractor.h"
-#include "common/task-context.h"
-#include "common/workspace.h"
-#include "lang_id/light-sentence-features.h"
-#include "lang_id/light-sentence.h"
-
-namespace libtextclassifier {
-namespace nlp_core {
-namespace lang_id {
-
-// Given a sentence, generates one FloatFeatureValue for each "relevant" Unicode
-// script (see below): each such feature indicates the script and the ratio of
-// UTF8 characters in that script, in the given sentence.
-//
-// What is a relevant script? Recognizing all 100+ Unicode scripts would
-// require too much code size and runtime. Instead, we focus only on a few
-// scripts that communicate a lot of language information: e.g., the use of
-// Hiragana characters almost always indicates Japanese, so Hiragana is a
-// "relevant" script for us. The Latin script is used by dozens of language, so
-// Latin is not relevant in this context.
-class RelevantScriptFeature : public LightSentenceFeature {
- public:
- // Idiomatic SAFT Setup() and Init().
- bool Setup(TaskContext *context) override;
- bool Init(TaskContext *context) override;
-
- // Appends the features computed from the sentence to the feature vector.
- void Evaluate(const WorkspaceSet &workspaces, const LightSentence &sentence,
- FeatureVector *result) const override;
-
- TC_DEFINE_REGISTRATION_METHOD("continuous-bag-of-relevant-scripts",
- RelevantScriptFeature);
-};
-
-} // namespace lang_id
-} // namespace nlp_core
-} // namespace libtextclassifier
-
-#endif // LIBTEXTCLASSIFIER_LANG_ID_RELEVANT_SCRIPT_FEATURE_H_
diff --git a/lang_id/script-detector.h b/lang_id/script-detector.h
deleted file mode 100644
index cf816ee..0000000
--- a/lang_id/script-detector.h
+++ /dev/null
@@ -1,162 +0,0 @@
-/*
- * Copyright (C) 2017 The Android Open Source Project
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#ifndef LIBTEXTCLASSIFIER_LANG_ID_SCRIPT_DETECTOR_H_
-#define LIBTEXTCLASSIFIER_LANG_ID_SCRIPT_DETECTOR_H_
-
-namespace libtextclassifier {
-namespace nlp_core {
-namespace lang_id {
-
-// Unicode scripts we care about. To get compact and fast code, we detect only
-// a few Unicode scripts that offer a strong indication about the language of
-// the text (e.g., Hiragana -> Japanese).
-enum Script {
- // Special value to indicate internal errors in the script detection code.
- kScriptError,
-
- // Special values for all Unicode scripts that we do not detect. One special
- // value for Unicode characters of 1, 2, 3, respectively 4 bytes (as we
- // already have that information, we use it). kScriptOtherUtf8OneByte means
- // ~Latin and kScriptOtherUtf8FourBytes means ~Han.
- kScriptOtherUtf8OneByte,
- kScriptOtherUtf8TwoBytes,
- kScriptOtherUtf8ThreeBytes,
- kScriptOtherUtf8FourBytes,
-
- kScriptGreek,
- kScriptCyrillic,
- kScriptHebrew,
- kScriptArabic,
- kScriptHangulJamo, // Used primarily for Korean.
- kScriptHiragana, // Used primarily for Japanese.
- kScriptKatakana, // Used primarily for Japanese.
-
- // Add new scripts here.
-
- // Do not add any script after kNumRelevantScripts. This value indicates the
- // number of elements in this enum Script (except this value) such that we can
- // easily iterate over the scripts.
- kNumRelevantScripts,
-};
-
-template<typename IntType>
-inline bool InRange(IntType value, IntType low, IntType hi) {
- return (value >= low) && (value <= hi);
-}
-
-// Returns Script for the UTF8 character that starts at address p.
-// Precondition: p points to a valid UTF8 character of num_bytes bytes.
-inline Script GetScript(const unsigned char *p, int num_bytes) {
- switch (num_bytes) {
- case 1:
- return kScriptOtherUtf8OneByte;
-
- case 2: {
- // 2-byte UTF8 characters have 11 bits of information. unsigned int has
- // at least 16 bits (http://en.cppreference.com/w/cpp/language/types) so
- // it's enough. It's also usually the fastest int type on the current
- // CPU, so it's better to use than int32.
- static const unsigned int kGreekStart = 0x370;
-
- // Commented out (unsued in the code): kGreekEnd = 0x3FF;
- static const unsigned int kCyrillicStart = 0x400;
- static const unsigned int kCyrillicEnd = 0x4FF;
- static const unsigned int kHebrewStart = 0x590;
-
- // Commented out (unsued in the code): kHebrewEnd = 0x5FF;
- static const unsigned int kArabicStart = 0x600;
- static const unsigned int kArabicEnd = 0x6FF;
- const unsigned int codepoint = ((p[0] & 0x1F) << 6) | (p[1] & 0x3F);
- if (codepoint > kCyrillicEnd) {
- if (codepoint >= kArabicStart) {
- if (codepoint <= kArabicEnd) {
- return kScriptArabic;
- }
- } else {
- // At this point, codepoint < kArabicStart = kHebrewEnd + 1, so
- // codepoint <= kHebrewEnd.
- if (codepoint >= kHebrewStart) {
- return kScriptHebrew;
- }
- }
- } else {
- if (codepoint >= kCyrillicStart) {
- return kScriptCyrillic;
- } else {
- // At this point, codepoint < kCyrillicStart = kGreekEnd + 1, so
- // codepoint <= kGreekEnd.
- if (codepoint >= kGreekStart) {
- return kScriptGreek;
- }
- }
- }
- return kScriptOtherUtf8TwoBytes;
- }
-
- case 3: {
- // 3-byte UTF8 characters have 16 bits of information. unsigned int has
- // at least 16 bits.
- static const unsigned int kHangulJamoStart = 0x1100;
- static const unsigned int kHangulJamoEnd = 0x11FF;
- static const unsigned int kHiraganaStart = 0x3041;
- static const unsigned int kHiraganaEnd = 0x309F;
-
- // Commented out (unsued in the code): kKatakanaStart = 0x30A0;
- static const unsigned int kKatakanaEnd = 0x30FF;
- const unsigned int codepoint =
- ((p[0] & 0x0F) << 12) | ((p[1] & 0x3F) << 6) | (p[2] & 0x3F);
- if (codepoint > kHiraganaEnd) {
- // On this branch, codepoint > kHiraganaEnd = kKatakanaStart - 1, so
- // codepoint >= kKatakanaStart.
- if (codepoint <= kKatakanaEnd) {
- return kScriptKatakana;
- }
- } else {
- if (codepoint >= kHiraganaStart) {
- return kScriptHiragana;
- } else {
- if (InRange(codepoint, kHangulJamoStart, kHangulJamoEnd)) {
- return kScriptHangulJamo;
- }
- }
- }
- return kScriptOtherUtf8ThreeBytes;
- }
-
- case 4:
- return kScriptOtherUtf8FourBytes;
-
- default:
- return kScriptError;
- }
-}
-
-// Returns Script for the UTF8 character that starts at address p. Similar to
-// the previous version of GetScript, except for "char" vs "unsigned char".
-// Most code works with "char *" pointers, ignoring the fact that char is
-// unsigned (by default) on most platforms, but signed on iOS. This code takes
-// care of making sure we always treat chars as unsigned.
-inline Script GetScript(const char *p, int num_bytes) {
- return GetScript(reinterpret_cast<const unsigned char *>(p),
- num_bytes);
-}
-
-} // namespace lang_id
-} // namespace nlp_core
-} // namespace libtextclassifier
-
-#endif // LIBTEXTCLASSIFIER_LANG_ID_SCRIPT_DETECTOR_H_
diff --git a/model-executor.cc b/model-executor.cc
new file mode 100644
index 0000000..69931cb
--- /dev/null
+++ b/model-executor.cc
@@ -0,0 +1,162 @@
+/*
+ * Copyright (C) 2017 The Android Open Source Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "model-executor.h"
+
+#include "quantization.h"
+#include "util/base/logging.h"
+
+namespace libtextclassifier2 {
+namespace internal {
+bool FromModelSpec(const tflite::Model* model_spec,
+ std::unique_ptr<const tflite::FlatBufferModel>* model) {
+ *model = tflite::FlatBufferModel::BuildFromModel(model_spec);
+ if (!(*model) || !(*model)->initialized()) {
+ TC_LOG(ERROR) << "Could not build TFLite model from a model spec. ";
+ return false;
+ }
+ return true;
+}
+} // namespace internal
+
+std::unique_ptr<tflite::Interpreter> ModelExecutor::CreateInterpreter() const {
+ std::unique_ptr<tflite::Interpreter> interpreter;
+ tflite::InterpreterBuilder(*model_, builtins_)(&interpreter);
+ return interpreter;
+}
+
+std::unique_ptr<TFLiteEmbeddingExecutor> TFLiteEmbeddingExecutor::Instance(
+ const flatbuffers::Vector<uint8_t>* model_spec_buffer, int embedding_size,
+ int quantization_bits) {
+ const tflite::Model* model_spec =
+ flatbuffers::GetRoot<tflite::Model>(model_spec_buffer->data());
+ flatbuffers::Verifier verifier(model_spec_buffer->data(),
+ model_spec_buffer->Length());
+ std::unique_ptr<const tflite::FlatBufferModel> model;
+ if (!model_spec->Verify(verifier) ||
+ !internal::FromModelSpec(model_spec, &model)) {
+ TC_LOG(ERROR) << "Could not load TFLite model.";
+ return nullptr;
+ }
+
+ std::unique_ptr<tflite::Interpreter> interpreter;
+ tflite::ops::builtin::BuiltinOpResolver builtins;
+ tflite::InterpreterBuilder(*model, builtins)(&interpreter);
+ if (!interpreter) {
+ TC_LOG(ERROR) << "Could not build TFLite interpreter for embeddings.";
+ return nullptr;
+ }
+
+ if (interpreter->tensors_size() != 2) {
+ return nullptr;
+ }
+ const TfLiteTensor* embeddings = interpreter->tensor(0);
+ if (embeddings->dims->size != 2) {
+ return nullptr;
+ }
+ int num_buckets = embeddings->dims->data[0];
+ const TfLiteTensor* scales = interpreter->tensor(1);
+ if (scales->dims->size != 2 || scales->dims->data[0] != num_buckets ||
+ scales->dims->data[1] != 1) {
+ return nullptr;
+ }
+ int bytes_per_embedding = embeddings->dims->data[1];
+ if (!CheckQuantizationParams(bytes_per_embedding, quantization_bits,
+ embedding_size)) {
+ TC_LOG(ERROR) << "Mismatch in quantization parameters.";
+ return nullptr;
+ }
+
+ return std::unique_ptr<TFLiteEmbeddingExecutor>(new TFLiteEmbeddingExecutor(
+ std::move(model), quantization_bits, num_buckets, bytes_per_embedding,
+ embedding_size, scales, embeddings, std::move(interpreter)));
+}
+
+TFLiteEmbeddingExecutor::TFLiteEmbeddingExecutor(
+ std::unique_ptr<const tflite::FlatBufferModel> model, int quantization_bits,
+ int num_buckets, int bytes_per_embedding, int output_embedding_size,
+ const TfLiteTensor* scales, const TfLiteTensor* embeddings,
+ std::unique_ptr<tflite::Interpreter> interpreter)
+ : model_(std::move(model)),
+ quantization_bits_(quantization_bits),
+ num_buckets_(num_buckets),
+ bytes_per_embedding_(bytes_per_embedding),
+ output_embedding_size_(output_embedding_size),
+ scales_(scales),
+ embeddings_(embeddings),
+ interpreter_(std::move(interpreter)) {}
+
+bool TFLiteEmbeddingExecutor::AddEmbedding(
+ const TensorView<int>& sparse_features, float* dest, int dest_size) const {
+ if (dest_size != output_embedding_size_) {
+ TC_LOG(ERROR) << "Mismatching dest_size and output_embedding_size: "
+ << dest_size << " " << output_embedding_size_;
+ return false;
+ }
+ const int num_sparse_features = sparse_features.size();
+ for (int i = 0; i < num_sparse_features; ++i) {
+ const int bucket_id = sparse_features.data()[i];
+ if (bucket_id >= num_buckets_) {
+ return false;
+ }
+
+ if (!DequantizeAdd(scales_->data.f, embeddings_->data.uint8,
+ bytes_per_embedding_, num_sparse_features,
+ quantization_bits_, bucket_id, dest, dest_size)) {
+ return false;
+ }
+ }
+ return true;
+}
+
+TensorView<float> ComputeLogitsHelper(const int input_index_features,
+ const int output_index_logits,
+ const TensorView<float>& features,
+ tflite::Interpreter* interpreter) {
+ if (!interpreter) {
+ return TensorView<float>::Invalid();
+ }
+ interpreter->ResizeInputTensor(input_index_features, features.shape());
+ if (interpreter->AllocateTensors() != kTfLiteOk) {
+ TC_VLOG(1) << "Allocation failed.";
+ return TensorView<float>::Invalid();
+ }
+
+ TfLiteTensor* features_tensor =
+ interpreter->tensor(interpreter->inputs()[input_index_features]);
+ int size = 1;
+ for (int i = 0; i < features_tensor->dims->size; ++i) {
+ size *= features_tensor->dims->data[i];
+ }
+ features.copy_to(features_tensor->data.f, size);
+
+ if (interpreter->Invoke() != kTfLiteOk) {
+ TC_VLOG(1) << "Interpreter failed.";
+ return TensorView<float>::Invalid();
+ }
+
+ TfLiteTensor* logits_tensor =
+ interpreter->tensor(interpreter->outputs()[output_index_logits]);
+
+ std::vector<int> output_shape(logits_tensor->dims->size);
+ for (int i = 0; i < logits_tensor->dims->size; ++i) {
+ output_shape[i] = logits_tensor->dims->data[i];
+ }
+
+ return TensorView<float>(logits_tensor->data.f, output_shape);
+}
+
+} // namespace libtextclassifier2
diff --git a/model-executor.h b/model-executor.h
new file mode 100644
index 0000000..ef6d36f
--- /dev/null
+++ b/model-executor.h
@@ -0,0 +1,137 @@
+/*
+ * Copyright (C) 2017 The Android Open Source Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+// Contains classes that can execute different models/parts of a model.
+
+#ifndef LIBTEXTCLASSIFIER_MODEL_EXECUTOR_H_
+#define LIBTEXTCLASSIFIER_MODEL_EXECUTOR_H_
+
+#include <memory>
+
+#include "tensor-view.h"
+#include "types.h"
+#include "util/base/logging.h"
+#include "tensorflow/contrib/lite/interpreter.h"
+#include "tensorflow/contrib/lite/kernels/register.h"
+#include "tensorflow/contrib/lite/model.h"
+
+namespace libtextclassifier2 {
+
+namespace internal {
+bool FromModelSpec(const tflite::Model* model_spec,
+ std::unique_ptr<const tflite::FlatBufferModel>* model);
+} // namespace internal
+
+// A helper function that given indices of feature and logits tensor, feature
+// values computes the logits using given interpreter.
+TensorView<float> ComputeLogitsHelper(const int input_index_features,
+ const int output_index_logits,
+ const TensorView<float>& features,
+ tflite::Interpreter* interpreter);
+
+// Executor for the text selection prediction and classification models.
+class ModelExecutor {
+ public:
+ static std::unique_ptr<const ModelExecutor> Instance(
+ const flatbuffers::Vector<uint8_t>* model_spec_buffer) {
+ const tflite::Model* model =
+ flatbuffers::GetRoot<tflite::Model>(model_spec_buffer->data());
+ flatbuffers::Verifier verifier(model_spec_buffer->data(),
+ model_spec_buffer->Length());
+ if (!model->Verify(verifier)) {
+ return nullptr;
+ }
+ return Instance(model);
+ }
+
+ static std::unique_ptr<const ModelExecutor> Instance(
+ const tflite::Model* model_spec) {
+ std::unique_ptr<const tflite::FlatBufferModel> model;
+ if (!internal::FromModelSpec(model_spec, &model)) {
+ return nullptr;
+ }
+ return std::unique_ptr<ModelExecutor>(new ModelExecutor(std::move(model)));
+ }
+
+ // Creates an Interpreter for the model that serves as a scratch-pad for the
+ // inference. The Interpreter is NOT thread-safe.
+ std::unique_ptr<tflite::Interpreter> CreateInterpreter() const;
+
+ TensorView<float> ComputeLogits(const TensorView<float>& features,
+ tflite::Interpreter* interpreter) const {
+ return ComputeLogitsHelper(kInputIndexFeatures, kOutputIndexLogits,
+ features, interpreter);
+ }
+
+ protected:
+ explicit ModelExecutor(std::unique_ptr<const tflite::FlatBufferModel> model)
+ : model_(std::move(model)) {}
+
+ static const int kInputIndexFeatures = 0;
+ static const int kOutputIndexLogits = 0;
+
+ std::unique_ptr<const tflite::FlatBufferModel> model_;
+ tflite::ops::builtin::BuiltinOpResolver builtins_;
+};
+
+// Executor for embedding sparse features into a dense vector.
+class EmbeddingExecutor {
+ public:
+ virtual ~EmbeddingExecutor() {}
+
+ // Embeds the sparse_features into a dense embedding and adds (+) it
+ // element-wise to the dest vector.
+ virtual bool AddEmbedding(const TensorView<int>& sparse_features, float* dest,
+ int dest_size) const = 0;
+
+ // Returns true when the model is ready to be used, false otherwise.
+ virtual bool IsReady() const { return true; }
+};
+
+class TFLiteEmbeddingExecutor : public EmbeddingExecutor {
+ public:
+ static std::unique_ptr<TFLiteEmbeddingExecutor> Instance(
+ const flatbuffers::Vector<uint8_t>* model_spec_buffer, int embedding_size,
+ int quantization_bits);
+
+ bool AddEmbedding(const TensorView<int>& sparse_features, float* dest,
+ int dest_size) const override;
+
+ protected:
+ explicit TFLiteEmbeddingExecutor(
+ std::unique_ptr<const tflite::FlatBufferModel> model,
+ int quantization_bits, int num_buckets, int bytes_per_embedding,
+ int output_embedding_size, const TfLiteTensor* scales,
+ const TfLiteTensor* embeddings,
+ std::unique_ptr<tflite::Interpreter> interpreter);
+
+ std::unique_ptr<const tflite::FlatBufferModel> model_;
+
+ int quantization_bits_;
+ int num_buckets_ = -1;
+ int bytes_per_embedding_ = -1;
+ int output_embedding_size_ = -1;
+ const TfLiteTensor* scales_ = nullptr;
+ const TfLiteTensor* embeddings_ = nullptr;
+
+ // NOTE: This interpreter is used in a read-only way (as a storage for the
+ // model params), thus is still thread-safe.
+ std::unique_ptr<tflite::Interpreter> interpreter_;
+};
+
+} // namespace libtextclassifier2
+
+#endif // LIBTEXTCLASSIFIER_MODEL_EXECUTOR_H_
diff --git a/model.fbs b/model.fbs
new file mode 100755
index 0000000..fb9778b
--- /dev/null
+++ b/model.fbs
@@ -0,0 +1,577 @@
+//
+// Copyright (C) 2017 The Android Open Source Project
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+// http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+//
+
+file_identifier "TC2 ";
+
+// The possible model modes, represents a bit field.
+namespace libtextclassifier2;
+enum ModeFlag : int {
+ NONE = 0,
+ ANNOTATION = 1,
+ CLASSIFICATION = 2,
+ ANNOTATION_AND_CLASSIFICATION = 3,
+ SELECTION = 4,
+ ANNOTATION_AND_SELECTION = 5,
+ CLASSIFICATION_AND_SELECTION = 6,
+ ALL = 7,
+}
+
+namespace libtextclassifier2;
+enum DatetimeExtractorType : int {
+ UNKNOWN_DATETIME_EXTRACTOR_TYPE = 0,
+ AM = 1,
+ PM = 2,
+ JANUARY = 3,
+ FEBRUARY = 4,
+ MARCH = 5,
+ APRIL = 6,
+ MAY = 7,
+ JUNE = 8,
+ JULY = 9,
+ AUGUST = 10,
+ SEPTEMBER = 11,
+ OCTOBER = 12,
+ NOVEMBER = 13,
+ DECEMBER = 14,
+ NEXT = 15,
+ NEXT_OR_SAME = 16,
+ LAST = 17,
+ NOW = 18,
+ TOMORROW = 19,
+ YESTERDAY = 20,
+ PAST = 21,
+ FUTURE = 22,
+ DAY = 23,
+ WEEK = 24,
+ MONTH = 25,
+ YEAR = 26,
+ MONDAY = 27,
+ TUESDAY = 28,
+ WEDNESDAY = 29,
+ THURSDAY = 30,
+ FRIDAY = 31,
+ SATURDAY = 32,
+ SUNDAY = 33,
+ DAYS = 34,
+ WEEKS = 35,
+ MONTHS = 36,
+ HOURS = 37,
+ MINUTES = 38,
+ SECONDS = 39,
+ YEARS = 40,
+ DIGITS = 41,
+ SIGNEDDIGITS = 42,
+ ZERO = 43,
+ ONE = 44,
+ TWO = 45,
+ THREE = 46,
+ FOUR = 47,
+ FIVE = 48,
+ SIX = 49,
+ SEVEN = 50,
+ EIGHT = 51,
+ NINE = 52,
+ TEN = 53,
+ ELEVEN = 54,
+ TWELVE = 55,
+ THIRTEEN = 56,
+ FOURTEEN = 57,
+ FIFTEEN = 58,
+ SIXTEEN = 59,
+ SEVENTEEN = 60,
+ EIGHTEEN = 61,
+ NINETEEN = 62,
+ TWENTY = 63,
+ THIRTY = 64,
+ FORTY = 65,
+ FIFTY = 66,
+ SIXTY = 67,
+ SEVENTY = 68,
+ EIGHTY = 69,
+ NINETY = 70,
+ HUNDRED = 71,
+ THOUSAND = 72,
+}
+
+namespace libtextclassifier2;
+enum DatetimeGroupType : int {
+ GROUP_UNKNOWN = 0,
+ GROUP_UNUSED = 1,
+ GROUP_YEAR = 2,
+ GROUP_MONTH = 3,
+ GROUP_DAY = 4,
+ GROUP_HOUR = 5,
+ GROUP_MINUTE = 6,
+ GROUP_SECOND = 7,
+ GROUP_AMPM = 8,
+ GROUP_RELATIONDISTANCE = 9,
+ GROUP_RELATION = 10,
+ GROUP_RELATIONTYPE = 11,
+
+ // Dummy groups serve just as an inflator of the selection. E.g. we might want
+ // to select more text than was contained in an envelope of all extractor
+ // spans.
+ GROUP_DUMMY1 = 12,
+
+ GROUP_DUMMY2 = 13,
+}
+
+namespace libtextclassifier2;
+table CompressedBuffer {
+ buffer:[ubyte];
+ uncompressed_size:int;
+}
+
+// Options for the model that predicts text selection.
+namespace libtextclassifier2;
+table SelectionModelOptions {
+ // If true, before the selection is returned, the unpaired brackets contained
+ // in the predicted selection are stripped from the both selection ends.
+ // The bracket codepoints are defined in the Unicode standard:
+ // http://www.unicode.org/Public/UNIDATA/BidiBrackets.txt
+ strip_unpaired_brackets:bool = 1;
+
+ // Number of hypothetical click positions on either side of the actual click
+ // to consider in order to enforce symmetry.
+ symmetry_context_size:int;
+
+ // Number of examples to bundle in one batch for inference.
+ batch_size:int = 1024;
+
+ // Whether to always classify a suggested selection or only on demand.
+ always_classify_suggested_selection:bool = 0;
+}
+
+// Options for the model that classifies a text selection.
+namespace libtextclassifier2;
+table ClassificationModelOptions {
+ // Limits for phone numbers.
+ phone_min_num_digits:int = 7;
+
+ phone_max_num_digits:int = 15;
+
+ // Limits for addresses.
+ address_min_num_tokens:int;
+
+ // Maximum number of tokens to attempt a classification (-1 is unlimited).
+ max_num_tokens:int = -1;
+}
+
+// List of regular expression matchers to check.
+namespace libtextclassifier2.RegexModel_;
+table Pattern {
+ // The name of the collection of a match.
+ collection_name:string;
+
+ // The pattern to check.
+ // Can specify a single capturing group used as match boundaries.
+ pattern:string;
+
+ // The modes for which to apply the patterns.
+ enabled_modes:libtextclassifier2.ModeFlag = ALL;
+
+ // The final score to assign to the results of this pattern.
+ target_classification_score:float = 1;
+
+ // Priority score used for conflict resolution with the other models.
+ priority_score:float = 0;
+
+ // If true, will use an approximate matching implementation implemented
+ // using Find() instead of the true Match(). This approximate matching will
+ // use the first Find() result and then check that it spans the whole input.
+ use_approximate_matching:bool = 0;
+
+ compressed_pattern:libtextclassifier2.CompressedBuffer;
+}
+
+namespace libtextclassifier2;
+table RegexModel {
+ patterns:[libtextclassifier2.RegexModel_.Pattern];
+}
+
+// List of regex patterns.
+namespace libtextclassifier2.DatetimeModelPattern_;
+table Regex {
+ pattern:string;
+
+ // The ith entry specifies the type of the ith capturing group.
+ // This is used to decide how the matched content has to be parsed.
+ groups:[libtextclassifier2.DatetimeGroupType];
+
+ compressed_pattern:libtextclassifier2.CompressedBuffer;
+}
+
+namespace libtextclassifier2;
+table DatetimeModelPattern {
+ regexes:[libtextclassifier2.DatetimeModelPattern_.Regex];
+
+ // List of locale indices in DatetimeModel that represent the locales that
+ // these patterns should be used for. If empty, can be used for all locales.
+ locales:[int];
+
+ // The final score to assign to the results of this pattern.
+ target_classification_score:float = 1;
+
+ // Priority score used for conflict resulution with the other models.
+ priority_score:float = 0;
+
+ // The modes for which to apply the patterns.
+ enabled_modes:libtextclassifier2.ModeFlag = ALL;
+}
+
+namespace libtextclassifier2;
+table DatetimeModelExtractor {
+ extractor:libtextclassifier2.DatetimeExtractorType;
+ pattern:string;
+ locales:[int];
+ compressed_pattern:libtextclassifier2.CompressedBuffer;
+}
+
+namespace libtextclassifier2;
+table DatetimeModel {
+ // List of BCP 47 locale strings representing all locales supported by the
+ // model. The individual patterns refer back to them using an index.
+ locales:[string];
+
+ patterns:[libtextclassifier2.DatetimeModelPattern];
+ extractors:[libtextclassifier2.DatetimeModelExtractor];
+
+ // If true, will use the extractors for determining the match location as
+ // opposed to using the location where the global pattern matched.
+ use_extractors_for_locating:bool = 1;
+
+ // List of locale ids, rules of whose are always run, after the requested
+ // ones.
+ default_locales:[int];
+}
+
+namespace libtextclassifier2.DatetimeModelLibrary_;
+table Item {
+ key:string;
+ value:libtextclassifier2.DatetimeModel;
+}
+
+// A set of named DateTime models.
+namespace libtextclassifier2;
+table DatetimeModelLibrary {
+ models:[libtextclassifier2.DatetimeModelLibrary_.Item];
+}
+
+// Options controlling the output of the Tensorflow Lite models.
+namespace libtextclassifier2;
+table ModelTriggeringOptions {
+ // Lower bound threshold for filtering annotation model outputs.
+ min_annotate_confidence:float = 0;
+
+ // The modes for which to enable the models.
+ enabled_modes:libtextclassifier2.ModeFlag = ALL;
+}
+
+// Options controlling the output of the classifier.
+namespace libtextclassifier2;
+table OutputOptions {
+ // Lists of collection names that will be filtered out at the output:
+ // - For annotation, the spans of given collection are simply dropped.
+ // - For classification, the result is mapped to the class "other".
+ // - For selection, the spans of given class are returned as
+ // single-selection.
+ filtered_collections_annotation:[string];
+
+ filtered_collections_classification:[string];
+ filtered_collections_selection:[string];
+}
+
+namespace libtextclassifier2;
+table Model {
+ // Comma-separated list of locales supported by the model as BCP 47 tags.
+ locales:string;
+
+ version:int;
+
+ // A name for the model that can be used for e.g. logging.
+ name:string;
+
+ selection_feature_options:libtextclassifier2.FeatureProcessorOptions;
+ classification_feature_options:libtextclassifier2.FeatureProcessorOptions;
+
+ // Tensorflow Lite models.
+ selection_model:[ubyte] (force_align: 16);
+
+ classification_model:[ubyte] (force_align: 16);
+ embedding_model:[ubyte] (force_align: 16);
+
+ // Options for the different models.
+ selection_options:libtextclassifier2.SelectionModelOptions;
+
+ classification_options:libtextclassifier2.ClassificationModelOptions;
+ regex_model:libtextclassifier2.RegexModel;
+ datetime_model:libtextclassifier2.DatetimeModel;
+
+ // Options controlling the output of the models.
+ triggering_options:libtextclassifier2.ModelTriggeringOptions;
+
+ // Global switch that controls if SuggestSelection(), ClassifyText() and
+ // Annotate() will run. If a mode is disabled it returns empty/no-op results.
+ enabled_modes:libtextclassifier2.ModeFlag = ALL;
+
+ // If true, will snap the selections that consist only of whitespaces to the
+ // containing suggested span. Otherwise, no suggestion is proposed, since the
+ // selections are not part of any token.
+ snap_whitespace_selections:bool = 1;
+
+ // Global configuration for the output of SuggestSelection(), ClassifyText()
+ // and Annotate().
+ output_options:libtextclassifier2.OutputOptions;
+}
+
+// Role of the codepoints in the range.
+namespace libtextclassifier2.TokenizationCodepointRange_;
+enum Role : int {
+ // Concatenates the codepoint to the current run of codepoints.
+ DEFAULT_ROLE = 0,
+
+ // Splits a run of codepoints before the current codepoint.
+ SPLIT_BEFORE = 1,
+
+ // Splits a run of codepoints after the current codepoint.
+ SPLIT_AFTER = 2,
+
+ // Each codepoint will be a separate token. Good e.g. for Chinese
+ // characters.
+ TOKEN_SEPARATOR = 3,
+
+ // Discards the codepoint.
+ DISCARD_CODEPOINT = 4,
+
+ // Common values:
+ // Splits on the characters and discards them. Good e.g. for the space
+ // character.
+ WHITESPACE_SEPARATOR = 7,
+}
+
+// Represents a codepoint range [start, end) with its role for tokenization.
+namespace libtextclassifier2;
+table TokenizationCodepointRange {
+ start:int;
+ end:int;
+ role:libtextclassifier2.TokenizationCodepointRange_.Role;
+
+ // Integer identifier of the script this range denotes. Negative values are
+ // reserved for Tokenizer's internal use.
+ script_id:int;
+}
+
+// Method for selecting the center token.
+namespace libtextclassifier2.FeatureProcessorOptions_;
+enum CenterTokenSelectionMethod : int {
+ DEFAULT_CENTER_TOKEN_METHOD = 0,
+
+ // Use click indices to determine the center token.
+ CENTER_TOKEN_FROM_CLICK = 1,
+
+ // Use selection indices to get a token range, and select the middle of it
+ // as the center token.
+ CENTER_TOKEN_MIDDLE_OF_SELECTION = 2,
+}
+
+// Controls the type of tokenization the model will use for the input text.
+namespace libtextclassifier2.FeatureProcessorOptions_;
+enum TokenizationType : int {
+ INVALID_TOKENIZATION_TYPE = 0,
+
+ // Use the internal tokenizer for tokenization.
+ INTERNAL_TOKENIZER = 1,
+
+ // Use ICU for tokenization.
+ ICU = 2,
+
+ // First apply ICU tokenization. Then identify stretches of tokens
+ // consisting only of codepoints in internal_tokenizer_codepoint_ranges
+ // and re-tokenize them using the internal tokenizer.
+ MIXED = 3,
+}
+
+// Range of codepoints start - end, where end is exclusive.
+namespace libtextclassifier2.FeatureProcessorOptions_;
+table CodepointRange {
+ start:int;
+ end:int;
+}
+
+// Bounds-sensitive feature extraction configuration.
+namespace libtextclassifier2.FeatureProcessorOptions_;
+table BoundsSensitiveFeatures {
+ // Enables the extraction of bounds-sensitive features, instead of the click
+ // context features.
+ enabled:bool;
+
+ // The numbers of tokens to extract in specific locations relative to the
+ // bounds.
+ // Immediately before the span.
+ num_tokens_before:int;
+
+ // Inside the span, aligned with the beginning.
+ num_tokens_inside_left:int;
+
+ // Inside the span, aligned with the end.
+ num_tokens_inside_right:int;
+
+ // Immediately after the span.
+ num_tokens_after:int;
+
+ // If true, also extracts the tokens of the entire span and adds up their
+ // features forming one "token" to include in the extracted features.
+ include_inside_bag:bool;
+
+ // If true, includes the selection length (in the number of tokens) as a
+ // feature.
+ include_inside_length:bool;
+
+ // If true, for selection, single token spans are not run through the model
+ // and their score is assumed to be zero.
+ score_single_token_spans_as_zero:bool;
+}
+
+namespace libtextclassifier2.FeatureProcessorOptions_;
+table AlternativeCollectionMapEntry {
+ key:string;
+ value:string;
+}
+
+namespace libtextclassifier2;
+table FeatureProcessorOptions {
+ // Number of buckets used for hashing charactergrams.
+ num_buckets:int = -1;
+
+ // Size of the embedding.
+ embedding_size:int = -1;
+
+ // Number of bits for quantization for embeddings.
+ embedding_quantization_bits:int = 8;
+
+ // Context size defines the number of words to the left and to the right of
+ // the selected word to be used as context. For example, if context size is
+ // N, then we take N words to the left and N words to the right of the
+ // selected word as its context.
+ context_size:int = -1;
+
+ // Maximum number of words of the context to select in total.
+ max_selection_span:int = -1;
+
+ // Orders of charactergrams to extract. E.g., 2 means character bigrams, 3
+ // character trigrams etc.
+ chargram_orders:[int];
+
+ // Maximum length of a word, in codepoints.
+ max_word_length:int = 20;
+
+ // If true, will use the unicode-aware functionality for extracting features.
+ unicode_aware_features:bool = 0;
+
+ // Whether to extract the token case feature.
+ extract_case_feature:bool = 0;
+
+ // Whether to extract the selection mask feature.
+ extract_selection_mask_feature:bool = 0;
+
+ // List of regexps to run over each token. For each regexp, if there is a
+ // match, a dense feature of 1.0 is emitted. Otherwise -1.0 is used.
+ regexp_feature:[string];
+
+ // Whether to remap all digits to a single number.
+ remap_digits:bool = 0;
+
+ // Whether to lower-case each token before generating hashgrams.
+ lowercase_tokens:bool;
+
+ // If true, the selection classifier output will contain only the selections
+ // that are feasible (e.g., those that are shorter than max_selection_span),
+ // if false, the output will be a complete cross-product of possible
+ // selections to the left and possible selections to the right, including the
+ // infeasible ones.
+ // NOTE: Exists mainly for compatibility with older models that were trained
+ // with the non-reduced output space.
+ selection_reduced_output_space:bool = 1;
+
+ // Collection names.
+ collections:[string];
+
+ // An index of collection in collections to be used if a collection name can't
+ // be mapped to an id.
+ default_collection:int = -1;
+
+ // If true, will split the input by lines, and only use the line that contains
+ // the clicked token.
+ only_use_line_with_click:bool = 0;
+
+ // If true, will split tokens that contain the selection boundary, at the
+ // position of the boundary.
+ // E.g. "foo{bar}@google.com" -> "foo", "bar", "@google.com"
+ split_tokens_on_selection_boundaries:bool = 0;
+
+ // Codepoint ranges that determine how different codepoints are tokenized.
+ // The ranges must not overlap.
+ tokenization_codepoint_config:[libtextclassifier2.TokenizationCodepointRange];
+
+ center_token_selection_method:libtextclassifier2.FeatureProcessorOptions_.CenterTokenSelectionMethod;
+
+ // If true, span boundaries will be snapped to containing tokens and not
+ // required to exactly match token boundaries.
+ snap_label_span_boundaries_to_containing_tokens:bool;
+
+ // A set of codepoint ranges supported by the model.
+ supported_codepoint_ranges:[libtextclassifier2.FeatureProcessorOptions_.CodepointRange];
+
+ // A set of codepoint ranges to use in the mixed tokenization mode to identify
+ // stretches of tokens to re-tokenize using the internal tokenizer.
+ internal_tokenizer_codepoint_ranges:[libtextclassifier2.FeatureProcessorOptions_.CodepointRange];
+
+ // Minimum ratio of supported codepoints in the input context. If the ratio
+ // is lower than this, the feature computation will fail.
+ min_supported_codepoint_ratio:float = 0;
+
+ // Used for versioning the format of features the model expects.
+ // - feature_version == 0:
+ // For each token the features consist of:
+ // - chargram embeddings
+ // - dense features
+ // Chargram embeddings for tokens are concatenated first together,
+ // and at the end, the dense features for the tokens are concatenated
+ // to it. So the resulting feature vector has two regions.
+ feature_version:int = 0;
+
+ tokenization_type:libtextclassifier2.FeatureProcessorOptions_.TokenizationType = INTERNAL_TOKENIZER;
+ icu_preserve_whitespace_tokens:bool = 0;
+
+ // List of codepoints that will be stripped from beginning and end of
+ // predicted spans.
+ ignored_span_boundary_codepoints:[int];
+
+ bounds_sensitive_features:libtextclassifier2.FeatureProcessorOptions_.BoundsSensitiveFeatures;
+
+ // List of allowed charactergrams. The extracted charactergrams are filtered
+ // using this list, and charactergrams that are not present are interpreted as
+ // out-of-vocabulary.
+ // If no allowed_chargrams are specified, all charactergrams are allowed.
+ // The field is typed as bytes type to allow non-UTF8 chargrams.
+ allowed_chargrams:[string];
+
+ // If true, tokens will be also split when the codepoint's script_id changes
+ // as defined in TokenizationCodepointRange.
+ tokenize_on_script_change:bool = 0;
+}
+
+root_type libtextclassifier2.Model;
diff --git a/model_generated.h b/model_generated.h
new file mode 100755
index 0000000..6ef75f6
--- /dev/null
+++ b/model_generated.h
@@ -0,0 +1,3718 @@
+/*
+ * Copyright (C) 2017 The Android Open Source Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+// automatically generated by the FlatBuffers compiler, do not modify
+
+
+#ifndef FLATBUFFERS_GENERATED_MODEL_LIBTEXTCLASSIFIER2_H_
+#define FLATBUFFERS_GENERATED_MODEL_LIBTEXTCLASSIFIER2_H_
+
+#include "flatbuffers/flatbuffers.h"
+
+namespace libtextclassifier2 {
+
+struct CompressedBuffer;
+struct CompressedBufferT;
+
+struct SelectionModelOptions;
+struct SelectionModelOptionsT;
+
+struct ClassificationModelOptions;
+struct ClassificationModelOptionsT;
+
+namespace RegexModel_ {
+
+struct Pattern;
+struct PatternT;
+
+} // namespace RegexModel_
+
+struct RegexModel;
+struct RegexModelT;
+
+namespace DatetimeModelPattern_ {
+
+struct Regex;
+struct RegexT;
+
+} // namespace DatetimeModelPattern_
+
+struct DatetimeModelPattern;
+struct DatetimeModelPatternT;
+
+struct DatetimeModelExtractor;
+struct DatetimeModelExtractorT;
+
+struct DatetimeModel;
+struct DatetimeModelT;
+
+namespace DatetimeModelLibrary_ {
+
+struct Item;
+struct ItemT;
+
+} // namespace DatetimeModelLibrary_
+
+struct DatetimeModelLibrary;
+struct DatetimeModelLibraryT;
+
+struct ModelTriggeringOptions;
+struct ModelTriggeringOptionsT;
+
+struct OutputOptions;
+struct OutputOptionsT;
+
+struct Model;
+struct ModelT;
+
+struct TokenizationCodepointRange;
+struct TokenizationCodepointRangeT;
+
+namespace FeatureProcessorOptions_ {
+
+struct CodepointRange;
+struct CodepointRangeT;
+
+struct BoundsSensitiveFeatures;
+struct BoundsSensitiveFeaturesT;
+
+struct AlternativeCollectionMapEntry;
+struct AlternativeCollectionMapEntryT;
+
+} // namespace FeatureProcessorOptions_
+
+struct FeatureProcessorOptions;
+struct FeatureProcessorOptionsT;
+
+enum ModeFlag {
+ ModeFlag_NONE = 0,
+ ModeFlag_ANNOTATION = 1,
+ ModeFlag_CLASSIFICATION = 2,
+ ModeFlag_ANNOTATION_AND_CLASSIFICATION = 3,
+ ModeFlag_SELECTION = 4,
+ ModeFlag_ANNOTATION_AND_SELECTION = 5,
+ ModeFlag_CLASSIFICATION_AND_SELECTION = 6,
+ ModeFlag_ALL = 7,
+ ModeFlag_MIN = ModeFlag_NONE,
+ ModeFlag_MAX = ModeFlag_ALL
+};
+
+inline ModeFlag (&EnumValuesModeFlag())[8] {
+ static ModeFlag values[] = {
+ ModeFlag_NONE,
+ ModeFlag_ANNOTATION,
+ ModeFlag_CLASSIFICATION,
+ ModeFlag_ANNOTATION_AND_CLASSIFICATION,
+ ModeFlag_SELECTION,
+ ModeFlag_ANNOTATION_AND_SELECTION,
+ ModeFlag_CLASSIFICATION_AND_SELECTION,
+ ModeFlag_ALL
+ };
+ return values;
+}
+
+inline const char **EnumNamesModeFlag() {
+ static const char *names[] = {
+ "NONE",
+ "ANNOTATION",
+ "CLASSIFICATION",
+ "ANNOTATION_AND_CLASSIFICATION",
+ "SELECTION",
+ "ANNOTATION_AND_SELECTION",
+ "CLASSIFICATION_AND_SELECTION",
+ "ALL",
+ nullptr
+ };
+ return names;
+}
+
+inline const char *EnumNameModeFlag(ModeFlag e) {
+ const size_t index = static_cast<int>(e);
+ return EnumNamesModeFlag()[index];
+}
+
+enum DatetimeExtractorType {
+ DatetimeExtractorType_UNKNOWN_DATETIME_EXTRACTOR_TYPE = 0,
+ DatetimeExtractorType_AM = 1,
+ DatetimeExtractorType_PM = 2,
+ DatetimeExtractorType_JANUARY = 3,
+ DatetimeExtractorType_FEBRUARY = 4,
+ DatetimeExtractorType_MARCH = 5,
+ DatetimeExtractorType_APRIL = 6,
+ DatetimeExtractorType_MAY = 7,
+ DatetimeExtractorType_JUNE = 8,
+ DatetimeExtractorType_JULY = 9,
+ DatetimeExtractorType_AUGUST = 10,
+ DatetimeExtractorType_SEPTEMBER = 11,
+ DatetimeExtractorType_OCTOBER = 12,
+ DatetimeExtractorType_NOVEMBER = 13,
+ DatetimeExtractorType_DECEMBER = 14,
+ DatetimeExtractorType_NEXT = 15,
+ DatetimeExtractorType_NEXT_OR_SAME = 16,
+ DatetimeExtractorType_LAST = 17,
+ DatetimeExtractorType_NOW = 18,
+ DatetimeExtractorType_TOMORROW = 19,
+ DatetimeExtractorType_YESTERDAY = 20,
+ DatetimeExtractorType_PAST = 21,
+ DatetimeExtractorType_FUTURE = 22,
+ DatetimeExtractorType_DAY = 23,
+ DatetimeExtractorType_WEEK = 24,
+ DatetimeExtractorType_MONTH = 25,
+ DatetimeExtractorType_YEAR = 26,
+ DatetimeExtractorType_MONDAY = 27,
+ DatetimeExtractorType_TUESDAY = 28,
+ DatetimeExtractorType_WEDNESDAY = 29,
+ DatetimeExtractorType_THURSDAY = 30,
+ DatetimeExtractorType_FRIDAY = 31,
+ DatetimeExtractorType_SATURDAY = 32,
+ DatetimeExtractorType_SUNDAY = 33,
+ DatetimeExtractorType_DAYS = 34,
+ DatetimeExtractorType_WEEKS = 35,
+ DatetimeExtractorType_MONTHS = 36,
+ DatetimeExtractorType_HOURS = 37,
+ DatetimeExtractorType_MINUTES = 38,
+ DatetimeExtractorType_SECONDS = 39,
+ DatetimeExtractorType_YEARS = 40,
+ DatetimeExtractorType_DIGITS = 41,
+ DatetimeExtractorType_SIGNEDDIGITS = 42,
+ DatetimeExtractorType_ZERO = 43,
+ DatetimeExtractorType_ONE = 44,
+ DatetimeExtractorType_TWO = 45,
+ DatetimeExtractorType_THREE = 46,
+ DatetimeExtractorType_FOUR = 47,
+ DatetimeExtractorType_FIVE = 48,
+ DatetimeExtractorType_SIX = 49,
+ DatetimeExtractorType_SEVEN = 50,
+ DatetimeExtractorType_EIGHT = 51,
+ DatetimeExtractorType_NINE = 52,
+ DatetimeExtractorType_TEN = 53,
+ DatetimeExtractorType_ELEVEN = 54,
+ DatetimeExtractorType_TWELVE = 55,
+ DatetimeExtractorType_THIRTEEN = 56,
+ DatetimeExtractorType_FOURTEEN = 57,
+ DatetimeExtractorType_FIFTEEN = 58,
+ DatetimeExtractorType_SIXTEEN = 59,
+ DatetimeExtractorType_SEVENTEEN = 60,
+ DatetimeExtractorType_EIGHTEEN = 61,
+ DatetimeExtractorType_NINETEEN = 62,
+ DatetimeExtractorType_TWENTY = 63,
+ DatetimeExtractorType_THIRTY = 64,
+ DatetimeExtractorType_FORTY = 65,
+ DatetimeExtractorType_FIFTY = 66,
+ DatetimeExtractorType_SIXTY = 67,
+ DatetimeExtractorType_SEVENTY = 68,
+ DatetimeExtractorType_EIGHTY = 69,
+ DatetimeExtractorType_NINETY = 70,
+ DatetimeExtractorType_HUNDRED = 71,
+ DatetimeExtractorType_THOUSAND = 72,
+ DatetimeExtractorType_MIN = DatetimeExtractorType_UNKNOWN_DATETIME_EXTRACTOR_TYPE,
+ DatetimeExtractorType_MAX = DatetimeExtractorType_THOUSAND
+};
+
+inline DatetimeExtractorType (&EnumValuesDatetimeExtractorType())[73] {
+ static DatetimeExtractorType values[] = {
+ DatetimeExtractorType_UNKNOWN_DATETIME_EXTRACTOR_TYPE,
+ DatetimeExtractorType_AM,
+ DatetimeExtractorType_PM,
+ DatetimeExtractorType_JANUARY,
+ DatetimeExtractorType_FEBRUARY,
+ DatetimeExtractorType_MARCH,
+ DatetimeExtractorType_APRIL,
+ DatetimeExtractorType_MAY,
+ DatetimeExtractorType_JUNE,
+ DatetimeExtractorType_JULY,
+ DatetimeExtractorType_AUGUST,
+ DatetimeExtractorType_SEPTEMBER,
+ DatetimeExtractorType_OCTOBER,
+ DatetimeExtractorType_NOVEMBER,
+ DatetimeExtractorType_DECEMBER,
+ DatetimeExtractorType_NEXT,
+ DatetimeExtractorType_NEXT_OR_SAME,
+ DatetimeExtractorType_LAST,
+ DatetimeExtractorType_NOW,
+ DatetimeExtractorType_TOMORROW,
+ DatetimeExtractorType_YESTERDAY,
+ DatetimeExtractorType_PAST,
+ DatetimeExtractorType_FUTURE,
+ DatetimeExtractorType_DAY,
+ DatetimeExtractorType_WEEK,
+ DatetimeExtractorType_MONTH,
+ DatetimeExtractorType_YEAR,
+ DatetimeExtractorType_MONDAY,
+ DatetimeExtractorType_TUESDAY,
+ DatetimeExtractorType_WEDNESDAY,
+ DatetimeExtractorType_THURSDAY,
+ DatetimeExtractorType_FRIDAY,
+ DatetimeExtractorType_SATURDAY,
+ DatetimeExtractorType_SUNDAY,
+ DatetimeExtractorType_DAYS,
+ DatetimeExtractorType_WEEKS,
+ DatetimeExtractorType_MONTHS,
+ DatetimeExtractorType_HOURS,
+ DatetimeExtractorType_MINUTES,
+ DatetimeExtractorType_SECONDS,
+ DatetimeExtractorType_YEARS,
+ DatetimeExtractorType_DIGITS,
+ DatetimeExtractorType_SIGNEDDIGITS,
+ DatetimeExtractorType_ZERO,
+ DatetimeExtractorType_ONE,
+ DatetimeExtractorType_TWO,
+ DatetimeExtractorType_THREE,
+ DatetimeExtractorType_FOUR,
+ DatetimeExtractorType_FIVE,
+ DatetimeExtractorType_SIX,
+ DatetimeExtractorType_SEVEN,
+ DatetimeExtractorType_EIGHT,
+ DatetimeExtractorType_NINE,
+ DatetimeExtractorType_TEN,
+ DatetimeExtractorType_ELEVEN,
+ DatetimeExtractorType_TWELVE,
+ DatetimeExtractorType_THIRTEEN,
+ DatetimeExtractorType_FOURTEEN,
+ DatetimeExtractorType_FIFTEEN,
+ DatetimeExtractorType_SIXTEEN,
+ DatetimeExtractorType_SEVENTEEN,
+ DatetimeExtractorType_EIGHTEEN,
+ DatetimeExtractorType_NINETEEN,
+ DatetimeExtractorType_TWENTY,
+ DatetimeExtractorType_THIRTY,
+ DatetimeExtractorType_FORTY,
+ DatetimeExtractorType_FIFTY,
+ DatetimeExtractorType_SIXTY,
+ DatetimeExtractorType_SEVENTY,
+ DatetimeExtractorType_EIGHTY,
+ DatetimeExtractorType_NINETY,
+ DatetimeExtractorType_HUNDRED,
+ DatetimeExtractorType_THOUSAND
+ };
+ return values;
+}
+
+inline const char **EnumNamesDatetimeExtractorType() {
+ static const char *names[] = {
+ "UNKNOWN_DATETIME_EXTRACTOR_TYPE",
+ "AM",
+ "PM",
+ "JANUARY",
+ "FEBRUARY",
+ "MARCH",
+ "APRIL",
+ "MAY",
+ "JUNE",
+ "JULY",
+ "AUGUST",
+ "SEPTEMBER",
+ "OCTOBER",
+ "NOVEMBER",
+ "DECEMBER",
+ "NEXT",
+ "NEXT_OR_SAME",
+ "LAST",
+ "NOW",
+ "TOMORROW",
+ "YESTERDAY",
+ "PAST",
+ "FUTURE",
+ "DAY",
+ "WEEK",
+ "MONTH",
+ "YEAR",
+ "MONDAY",
+ "TUESDAY",
+ "WEDNESDAY",
+ "THURSDAY",
+ "FRIDAY",
+ "SATURDAY",
+ "SUNDAY",
+ "DAYS",
+ "WEEKS",
+ "MONTHS",
+ "HOURS",
+ "MINUTES",
+ "SECONDS",
+ "YEARS",
+ "DIGITS",
+ "SIGNEDDIGITS",
+ "ZERO",
+ "ONE",
+ "TWO",
+ "THREE",
+ "FOUR",
+ "FIVE",
+ "SIX",
+ "SEVEN",
+ "EIGHT",
+ "NINE",
+ "TEN",
+ "ELEVEN",
+ "TWELVE",
+ "THIRTEEN",
+ "FOURTEEN",
+ "FIFTEEN",
+ "SIXTEEN",
+ "SEVENTEEN",
+ "EIGHTEEN",
+ "NINETEEN",
+ "TWENTY",
+ "THIRTY",
+ "FORTY",
+ "FIFTY",
+ "SIXTY",
+ "SEVENTY",
+ "EIGHTY",
+ "NINETY",
+ "HUNDRED",
+ "THOUSAND",
+ nullptr
+ };
+ return names;
+}
+
+inline const char *EnumNameDatetimeExtractorType(DatetimeExtractorType e) {
+ const size_t index = static_cast<int>(e);
+ return EnumNamesDatetimeExtractorType()[index];
+}
+
+enum DatetimeGroupType {
+ DatetimeGroupType_GROUP_UNKNOWN = 0,
+ DatetimeGroupType_GROUP_UNUSED = 1,
+ DatetimeGroupType_GROUP_YEAR = 2,
+ DatetimeGroupType_GROUP_MONTH = 3,
+ DatetimeGroupType_GROUP_DAY = 4,
+ DatetimeGroupType_GROUP_HOUR = 5,
+ DatetimeGroupType_GROUP_MINUTE = 6,
+ DatetimeGroupType_GROUP_SECOND = 7,
+ DatetimeGroupType_GROUP_AMPM = 8,
+ DatetimeGroupType_GROUP_RELATIONDISTANCE = 9,
+ DatetimeGroupType_GROUP_RELATION = 10,
+ DatetimeGroupType_GROUP_RELATIONTYPE = 11,
+ DatetimeGroupType_GROUP_DUMMY1 = 12,
+ DatetimeGroupType_GROUP_DUMMY2 = 13,
+ DatetimeGroupType_MIN = DatetimeGroupType_GROUP_UNKNOWN,
+ DatetimeGroupType_MAX = DatetimeGroupType_GROUP_DUMMY2
+};
+
+inline DatetimeGroupType (&EnumValuesDatetimeGroupType())[14] {
+ static DatetimeGroupType values[] = {
+ DatetimeGroupType_GROUP_UNKNOWN,
+ DatetimeGroupType_GROUP_UNUSED,
+ DatetimeGroupType_GROUP_YEAR,
+ DatetimeGroupType_GROUP_MONTH,
+ DatetimeGroupType_GROUP_DAY,
+ DatetimeGroupType_GROUP_HOUR,
+ DatetimeGroupType_GROUP_MINUTE,
+ DatetimeGroupType_GROUP_SECOND,
+ DatetimeGroupType_GROUP_AMPM,
+ DatetimeGroupType_GROUP_RELATIONDISTANCE,
+ DatetimeGroupType_GROUP_RELATION,
+ DatetimeGroupType_GROUP_RELATIONTYPE,
+ DatetimeGroupType_GROUP_DUMMY1,
+ DatetimeGroupType_GROUP_DUMMY2
+ };
+ return values;
+}
+
+inline const char **EnumNamesDatetimeGroupType() {
+ static const char *names[] = {
+ "GROUP_UNKNOWN",
+ "GROUP_UNUSED",
+ "GROUP_YEAR",
+ "GROUP_MONTH",
+ "GROUP_DAY",
+ "GROUP_HOUR",
+ "GROUP_MINUTE",
+ "GROUP_SECOND",
+ "GROUP_AMPM",
+ "GROUP_RELATIONDISTANCE",
+ "GROUP_RELATION",
+ "GROUP_RELATIONTYPE",
+ "GROUP_DUMMY1",
+ "GROUP_DUMMY2",
+ nullptr
+ };
+ return names;
+}
+
+inline const char *EnumNameDatetimeGroupType(DatetimeGroupType e) {
+ const size_t index = static_cast<int>(e);
+ return EnumNamesDatetimeGroupType()[index];
+}
+
+namespace TokenizationCodepointRange_ {
+
+enum Role {
+ Role_DEFAULT_ROLE = 0,
+ Role_SPLIT_BEFORE = 1,
+ Role_SPLIT_AFTER = 2,
+ Role_TOKEN_SEPARATOR = 3,
+ Role_DISCARD_CODEPOINT = 4,
+ Role_WHITESPACE_SEPARATOR = 7,
+ Role_MIN = Role_DEFAULT_ROLE,
+ Role_MAX = Role_WHITESPACE_SEPARATOR
+};
+
+inline Role (&EnumValuesRole())[6] {
+ static Role values[] = {
+ Role_DEFAULT_ROLE,
+ Role_SPLIT_BEFORE,
+ Role_SPLIT_AFTER,
+ Role_TOKEN_SEPARATOR,
+ Role_DISCARD_CODEPOINT,
+ Role_WHITESPACE_SEPARATOR
+ };
+ return values;
+}
+
+inline const char **EnumNamesRole() {
+ static const char *names[] = {
+ "DEFAULT_ROLE",
+ "SPLIT_BEFORE",
+ "SPLIT_AFTER",
+ "TOKEN_SEPARATOR",
+ "DISCARD_CODEPOINT",
+ "",
+ "",
+ "WHITESPACE_SEPARATOR",
+ nullptr
+ };
+ return names;
+}
+
+inline const char *EnumNameRole(Role e) {
+ const size_t index = static_cast<int>(e);
+ return EnumNamesRole()[index];
+}
+
+} // namespace TokenizationCodepointRange_
+
+namespace FeatureProcessorOptions_ {
+
+enum CenterTokenSelectionMethod {
+ CenterTokenSelectionMethod_DEFAULT_CENTER_TOKEN_METHOD = 0,
+ CenterTokenSelectionMethod_CENTER_TOKEN_FROM_CLICK = 1,
+ CenterTokenSelectionMethod_CENTER_TOKEN_MIDDLE_OF_SELECTION = 2,
+ CenterTokenSelectionMethod_MIN = CenterTokenSelectionMethod_DEFAULT_CENTER_TOKEN_METHOD,
+ CenterTokenSelectionMethod_MAX = CenterTokenSelectionMethod_CENTER_TOKEN_MIDDLE_OF_SELECTION
+};
+
+inline CenterTokenSelectionMethod (&EnumValuesCenterTokenSelectionMethod())[3] {
+ static CenterTokenSelectionMethod values[] = {
+ CenterTokenSelectionMethod_DEFAULT_CENTER_TOKEN_METHOD,
+ CenterTokenSelectionMethod_CENTER_TOKEN_FROM_CLICK,
+ CenterTokenSelectionMethod_CENTER_TOKEN_MIDDLE_OF_SELECTION
+ };
+ return values;
+}
+
+inline const char **EnumNamesCenterTokenSelectionMethod() {
+ static const char *names[] = {
+ "DEFAULT_CENTER_TOKEN_METHOD",
+ "CENTER_TOKEN_FROM_CLICK",
+ "CENTER_TOKEN_MIDDLE_OF_SELECTION",
+ nullptr
+ };
+ return names;
+}
+
+inline const char *EnumNameCenterTokenSelectionMethod(CenterTokenSelectionMethod e) {
+ const size_t index = static_cast<int>(e);
+ return EnumNamesCenterTokenSelectionMethod()[index];
+}
+
+enum TokenizationType {
+ TokenizationType_INVALID_TOKENIZATION_TYPE = 0,
+ TokenizationType_INTERNAL_TOKENIZER = 1,
+ TokenizationType_ICU = 2,
+ TokenizationType_MIXED = 3,
+ TokenizationType_MIN = TokenizationType_INVALID_TOKENIZATION_TYPE,
+ TokenizationType_MAX = TokenizationType_MIXED
+};
+
+inline TokenizationType (&EnumValuesTokenizationType())[4] {
+ static TokenizationType values[] = {
+ TokenizationType_INVALID_TOKENIZATION_TYPE,
+ TokenizationType_INTERNAL_TOKENIZER,
+ TokenizationType_ICU,
+ TokenizationType_MIXED
+ };
+ return values;
+}
+
+inline const char **EnumNamesTokenizationType() {
+ static const char *names[] = {
+ "INVALID_TOKENIZATION_TYPE",
+ "INTERNAL_TOKENIZER",
+ "ICU",
+ "MIXED",
+ nullptr
+ };
+ return names;
+}
+
+inline const char *EnumNameTokenizationType(TokenizationType e) {
+ const size_t index = static_cast<int>(e);
+ return EnumNamesTokenizationType()[index];
+}
+
+} // namespace FeatureProcessorOptions_
+
+struct CompressedBufferT : public flatbuffers::NativeTable {
+ typedef CompressedBuffer TableType;
+ std::vector<uint8_t> buffer;
+ int32_t uncompressed_size;
+ CompressedBufferT()
+ : uncompressed_size(0) {
+ }
+};
+
+struct CompressedBuffer FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table {
+ typedef CompressedBufferT NativeTableType;
+ enum {
+ VT_BUFFER = 4,
+ VT_UNCOMPRESSED_SIZE = 6
+ };
+ const flatbuffers::Vector<uint8_t> *buffer() const {
+ return GetPointer<const flatbuffers::Vector<uint8_t> *>(VT_BUFFER);
+ }
+ int32_t uncompressed_size() const {
+ return GetField<int32_t>(VT_UNCOMPRESSED_SIZE, 0);
+ }
+ bool Verify(flatbuffers::Verifier &verifier) const {
+ return VerifyTableStart(verifier) &&
+ VerifyOffset(verifier, VT_BUFFER) &&
+ verifier.Verify(buffer()) &&
+ VerifyField<int32_t>(verifier, VT_UNCOMPRESSED_SIZE) &&
+ verifier.EndTable();
+ }
+ CompressedBufferT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ void UnPackTo(CompressedBufferT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ static flatbuffers::Offset<CompressedBuffer> Pack(flatbuffers::FlatBufferBuilder &_fbb, const CompressedBufferT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
+};
+
+struct CompressedBufferBuilder {
+ flatbuffers::FlatBufferBuilder &fbb_;
+ flatbuffers::uoffset_t start_;
+ void add_buffer(flatbuffers::Offset<flatbuffers::Vector<uint8_t>> buffer) {
+ fbb_.AddOffset(CompressedBuffer::VT_BUFFER, buffer);
+ }
+ void add_uncompressed_size(int32_t uncompressed_size) {
+ fbb_.AddElement<int32_t>(CompressedBuffer::VT_UNCOMPRESSED_SIZE, uncompressed_size, 0);
+ }
+ explicit CompressedBufferBuilder(flatbuffers::FlatBufferBuilder &_fbb)
+ : fbb_(_fbb) {
+ start_ = fbb_.StartTable();
+ }
+ CompressedBufferBuilder &operator=(const CompressedBufferBuilder &);
+ flatbuffers::Offset<CompressedBuffer> Finish() {
+ const auto end = fbb_.EndTable(start_);
+ auto o = flatbuffers::Offset<CompressedBuffer>(end);
+ return o;
+ }
+};
+
+inline flatbuffers::Offset<CompressedBuffer> CreateCompressedBuffer(
+ flatbuffers::FlatBufferBuilder &_fbb,
+ flatbuffers::Offset<flatbuffers::Vector<uint8_t>> buffer = 0,
+ int32_t uncompressed_size = 0) {
+ CompressedBufferBuilder builder_(_fbb);
+ builder_.add_uncompressed_size(uncompressed_size);
+ builder_.add_buffer(buffer);
+ return builder_.Finish();
+}
+
+inline flatbuffers::Offset<CompressedBuffer> CreateCompressedBufferDirect(
+ flatbuffers::FlatBufferBuilder &_fbb,
+ const std::vector<uint8_t> *buffer = nullptr,
+ int32_t uncompressed_size = 0) {
+ return libtextclassifier2::CreateCompressedBuffer(
+ _fbb,
+ buffer ? _fbb.CreateVector<uint8_t>(*buffer) : 0,
+ uncompressed_size);
+}
+
+flatbuffers::Offset<CompressedBuffer> CreateCompressedBuffer(flatbuffers::FlatBufferBuilder &_fbb, const CompressedBufferT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
+
+struct SelectionModelOptionsT : public flatbuffers::NativeTable {
+ typedef SelectionModelOptions TableType;
+ bool strip_unpaired_brackets;
+ int32_t symmetry_context_size;
+ int32_t batch_size;
+ bool always_classify_suggested_selection;
+ SelectionModelOptionsT()
+ : strip_unpaired_brackets(true),
+ symmetry_context_size(0),
+ batch_size(1024),
+ always_classify_suggested_selection(false) {
+ }
+};
+
+struct SelectionModelOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table {
+ typedef SelectionModelOptionsT NativeTableType;
+ enum {
+ VT_STRIP_UNPAIRED_BRACKETS = 4,
+ VT_SYMMETRY_CONTEXT_SIZE = 6,
+ VT_BATCH_SIZE = 8,
+ VT_ALWAYS_CLASSIFY_SUGGESTED_SELECTION = 10
+ };
+ bool strip_unpaired_brackets() const {
+ return GetField<uint8_t>(VT_STRIP_UNPAIRED_BRACKETS, 1) != 0;
+ }
+ int32_t symmetry_context_size() const {
+ return GetField<int32_t>(VT_SYMMETRY_CONTEXT_SIZE, 0);
+ }
+ int32_t batch_size() const {
+ return GetField<int32_t>(VT_BATCH_SIZE, 1024);
+ }
+ bool always_classify_suggested_selection() const {
+ return GetField<uint8_t>(VT_ALWAYS_CLASSIFY_SUGGESTED_SELECTION, 0) != 0;
+ }
+ bool Verify(flatbuffers::Verifier &verifier) const {
+ return VerifyTableStart(verifier) &&
+ VerifyField<uint8_t>(verifier, VT_STRIP_UNPAIRED_BRACKETS) &&
+ VerifyField<int32_t>(verifier, VT_SYMMETRY_CONTEXT_SIZE) &&
+ VerifyField<int32_t>(verifier, VT_BATCH_SIZE) &&
+ VerifyField<uint8_t>(verifier, VT_ALWAYS_CLASSIFY_SUGGESTED_SELECTION) &&
+ verifier.EndTable();
+ }
+ SelectionModelOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ void UnPackTo(SelectionModelOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ static flatbuffers::Offset<SelectionModelOptions> Pack(flatbuffers::FlatBufferBuilder &_fbb, const SelectionModelOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
+};
+
+struct SelectionModelOptionsBuilder {
+ flatbuffers::FlatBufferBuilder &fbb_;
+ flatbuffers::uoffset_t start_;
+ void add_strip_unpaired_brackets(bool strip_unpaired_brackets) {
+ fbb_.AddElement<uint8_t>(SelectionModelOptions::VT_STRIP_UNPAIRED_BRACKETS, static_cast<uint8_t>(strip_unpaired_brackets), 1);
+ }
+ void add_symmetry_context_size(int32_t symmetry_context_size) {
+ fbb_.AddElement<int32_t>(SelectionModelOptions::VT_SYMMETRY_CONTEXT_SIZE, symmetry_context_size, 0);
+ }
+ void add_batch_size(int32_t batch_size) {
+ fbb_.AddElement<int32_t>(SelectionModelOptions::VT_BATCH_SIZE, batch_size, 1024);
+ }
+ void add_always_classify_suggested_selection(bool always_classify_suggested_selection) {
+ fbb_.AddElement<uint8_t>(SelectionModelOptions::VT_ALWAYS_CLASSIFY_SUGGESTED_SELECTION, static_cast<uint8_t>(always_classify_suggested_selection), 0);
+ }
+ explicit SelectionModelOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb)
+ : fbb_(_fbb) {
+ start_ = fbb_.StartTable();
+ }
+ SelectionModelOptionsBuilder &operator=(const SelectionModelOptionsBuilder &);
+ flatbuffers::Offset<SelectionModelOptions> Finish() {
+ const auto end = fbb_.EndTable(start_);
+ auto o = flatbuffers::Offset<SelectionModelOptions>(end);
+ return o;
+ }
+};
+
+inline flatbuffers::Offset<SelectionModelOptions> CreateSelectionModelOptions(
+ flatbuffers::FlatBufferBuilder &_fbb,
+ bool strip_unpaired_brackets = true,
+ int32_t symmetry_context_size = 0,
+ int32_t batch_size = 1024,
+ bool always_classify_suggested_selection = false) {
+ SelectionModelOptionsBuilder builder_(_fbb);
+ builder_.add_batch_size(batch_size);
+ builder_.add_symmetry_context_size(symmetry_context_size);
+ builder_.add_always_classify_suggested_selection(always_classify_suggested_selection);
+ builder_.add_strip_unpaired_brackets(strip_unpaired_brackets);
+ return builder_.Finish();
+}
+
+flatbuffers::Offset<SelectionModelOptions> CreateSelectionModelOptions(flatbuffers::FlatBufferBuilder &_fbb, const SelectionModelOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
+
+struct ClassificationModelOptionsT : public flatbuffers::NativeTable {
+ typedef ClassificationModelOptions TableType;
+ int32_t phone_min_num_digits;
+ int32_t phone_max_num_digits;
+ int32_t address_min_num_tokens;
+ int32_t max_num_tokens;
+ ClassificationModelOptionsT()
+ : phone_min_num_digits(7),
+ phone_max_num_digits(15),
+ address_min_num_tokens(0),
+ max_num_tokens(-1) {
+ }
+};
+
+struct ClassificationModelOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table {
+ typedef ClassificationModelOptionsT NativeTableType;
+ enum {
+ VT_PHONE_MIN_NUM_DIGITS = 4,
+ VT_PHONE_MAX_NUM_DIGITS = 6,
+ VT_ADDRESS_MIN_NUM_TOKENS = 8,
+ VT_MAX_NUM_TOKENS = 10
+ };
+ int32_t phone_min_num_digits() const {
+ return GetField<int32_t>(VT_PHONE_MIN_NUM_DIGITS, 7);
+ }
+ int32_t phone_max_num_digits() const {
+ return GetField<int32_t>(VT_PHONE_MAX_NUM_DIGITS, 15);
+ }
+ int32_t address_min_num_tokens() const {
+ return GetField<int32_t>(VT_ADDRESS_MIN_NUM_TOKENS, 0);
+ }
+ int32_t max_num_tokens() const {
+ return GetField<int32_t>(VT_MAX_NUM_TOKENS, -1);
+ }
+ bool Verify(flatbuffers::Verifier &verifier) const {
+ return VerifyTableStart(verifier) &&
+ VerifyField<int32_t>(verifier, VT_PHONE_MIN_NUM_DIGITS) &&
+ VerifyField<int32_t>(verifier, VT_PHONE_MAX_NUM_DIGITS) &&
+ VerifyField<int32_t>(verifier, VT_ADDRESS_MIN_NUM_TOKENS) &&
+ VerifyField<int32_t>(verifier, VT_MAX_NUM_TOKENS) &&
+ verifier.EndTable();
+ }
+ ClassificationModelOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ void UnPackTo(ClassificationModelOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ static flatbuffers::Offset<ClassificationModelOptions> Pack(flatbuffers::FlatBufferBuilder &_fbb, const ClassificationModelOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
+};
+
+struct ClassificationModelOptionsBuilder {
+ flatbuffers::FlatBufferBuilder &fbb_;
+ flatbuffers::uoffset_t start_;
+ void add_phone_min_num_digits(int32_t phone_min_num_digits) {
+ fbb_.AddElement<int32_t>(ClassificationModelOptions::VT_PHONE_MIN_NUM_DIGITS, phone_min_num_digits, 7);
+ }
+ void add_phone_max_num_digits(int32_t phone_max_num_digits) {
+ fbb_.AddElement<int32_t>(ClassificationModelOptions::VT_PHONE_MAX_NUM_DIGITS, phone_max_num_digits, 15);
+ }
+ void add_address_min_num_tokens(int32_t address_min_num_tokens) {
+ fbb_.AddElement<int32_t>(ClassificationModelOptions::VT_ADDRESS_MIN_NUM_TOKENS, address_min_num_tokens, 0);
+ }
+ void add_max_num_tokens(int32_t max_num_tokens) {
+ fbb_.AddElement<int32_t>(ClassificationModelOptions::VT_MAX_NUM_TOKENS, max_num_tokens, -1);
+ }
+ explicit ClassificationModelOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb)
+ : fbb_(_fbb) {
+ start_ = fbb_.StartTable();
+ }
+ ClassificationModelOptionsBuilder &operator=(const ClassificationModelOptionsBuilder &);
+ flatbuffers::Offset<ClassificationModelOptions> Finish() {
+ const auto end = fbb_.EndTable(start_);
+ auto o = flatbuffers::Offset<ClassificationModelOptions>(end);
+ return o;
+ }
+};
+
+inline flatbuffers::Offset<ClassificationModelOptions> CreateClassificationModelOptions(
+ flatbuffers::FlatBufferBuilder &_fbb,
+ int32_t phone_min_num_digits = 7,
+ int32_t phone_max_num_digits = 15,
+ int32_t address_min_num_tokens = 0,
+ int32_t max_num_tokens = -1) {
+ ClassificationModelOptionsBuilder builder_(_fbb);
+ builder_.add_max_num_tokens(max_num_tokens);
+ builder_.add_address_min_num_tokens(address_min_num_tokens);
+ builder_.add_phone_max_num_digits(phone_max_num_digits);
+ builder_.add_phone_min_num_digits(phone_min_num_digits);
+ return builder_.Finish();
+}
+
+flatbuffers::Offset<ClassificationModelOptions> CreateClassificationModelOptions(flatbuffers::FlatBufferBuilder &_fbb, const ClassificationModelOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
+
+namespace RegexModel_ {
+
+struct PatternT : public flatbuffers::NativeTable {
+ typedef Pattern TableType;
+ std::string collection_name;
+ std::string pattern;
+ libtextclassifier2::ModeFlag enabled_modes;
+ float target_classification_score;
+ float priority_score;
+ bool use_approximate_matching;
+ std::unique_ptr<libtextclassifier2::CompressedBufferT> compressed_pattern;
+ PatternT()
+ : enabled_modes(libtextclassifier2::ModeFlag_ALL),
+ target_classification_score(1.0f),
+ priority_score(0.0f),
+ use_approximate_matching(false) {
+ }
+};
+
+struct Pattern FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table {
+ typedef PatternT NativeTableType;
+ enum {
+ VT_COLLECTION_NAME = 4,
+ VT_PATTERN = 6,
+ VT_ENABLED_MODES = 8,
+ VT_TARGET_CLASSIFICATION_SCORE = 10,
+ VT_PRIORITY_SCORE = 12,
+ VT_USE_APPROXIMATE_MATCHING = 14,
+ VT_COMPRESSED_PATTERN = 16
+ };
+ const flatbuffers::String *collection_name() const {
+ return GetPointer<const flatbuffers::String *>(VT_COLLECTION_NAME);
+ }
+ const flatbuffers::String *pattern() const {
+ return GetPointer<const flatbuffers::String *>(VT_PATTERN);
+ }
+ libtextclassifier2::ModeFlag enabled_modes() const {
+ return static_cast<libtextclassifier2::ModeFlag>(GetField<int32_t>(VT_ENABLED_MODES, 7));
+ }
+ float target_classification_score() const {
+ return GetField<float>(VT_TARGET_CLASSIFICATION_SCORE, 1.0f);
+ }
+ float priority_score() const {
+ return GetField<float>(VT_PRIORITY_SCORE, 0.0f);
+ }
+ bool use_approximate_matching() const {
+ return GetField<uint8_t>(VT_USE_APPROXIMATE_MATCHING, 0) != 0;
+ }
+ const libtextclassifier2::CompressedBuffer *compressed_pattern() const {
+ return GetPointer<const libtextclassifier2::CompressedBuffer *>(VT_COMPRESSED_PATTERN);
+ }
+ bool Verify(flatbuffers::Verifier &verifier) const {
+ return VerifyTableStart(verifier) &&
+ VerifyOffset(verifier, VT_COLLECTION_NAME) &&
+ verifier.Verify(collection_name()) &&
+ VerifyOffset(verifier, VT_PATTERN) &&
+ verifier.Verify(pattern()) &&
+ VerifyField<int32_t>(verifier, VT_ENABLED_MODES) &&
+ VerifyField<float>(verifier, VT_TARGET_CLASSIFICATION_SCORE) &&
+ VerifyField<float>(verifier, VT_PRIORITY_SCORE) &&
+ VerifyField<uint8_t>(verifier, VT_USE_APPROXIMATE_MATCHING) &&
+ VerifyOffset(verifier, VT_COMPRESSED_PATTERN) &&
+ verifier.VerifyTable(compressed_pattern()) &&
+ verifier.EndTable();
+ }
+ PatternT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ void UnPackTo(PatternT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ static flatbuffers::Offset<Pattern> Pack(flatbuffers::FlatBufferBuilder &_fbb, const PatternT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
+};
+
+struct PatternBuilder {
+ flatbuffers::FlatBufferBuilder &fbb_;
+ flatbuffers::uoffset_t start_;
+ void add_collection_name(flatbuffers::Offset<flatbuffers::String> collection_name) {
+ fbb_.AddOffset(Pattern::VT_COLLECTION_NAME, collection_name);
+ }
+ void add_pattern(flatbuffers::Offset<flatbuffers::String> pattern) {
+ fbb_.AddOffset(Pattern::VT_PATTERN, pattern);
+ }
+ void add_enabled_modes(libtextclassifier2::ModeFlag enabled_modes) {
+ fbb_.AddElement<int32_t>(Pattern::VT_ENABLED_MODES, static_cast<int32_t>(enabled_modes), 7);
+ }
+ void add_target_classification_score(float target_classification_score) {
+ fbb_.AddElement<float>(Pattern::VT_TARGET_CLASSIFICATION_SCORE, target_classification_score, 1.0f);
+ }
+ void add_priority_score(float priority_score) {
+ fbb_.AddElement<float>(Pattern::VT_PRIORITY_SCORE, priority_score, 0.0f);
+ }
+ void add_use_approximate_matching(bool use_approximate_matching) {
+ fbb_.AddElement<uint8_t>(Pattern::VT_USE_APPROXIMATE_MATCHING, static_cast<uint8_t>(use_approximate_matching), 0);
+ }
+ void add_compressed_pattern(flatbuffers::Offset<libtextclassifier2::CompressedBuffer> compressed_pattern) {
+ fbb_.AddOffset(Pattern::VT_COMPRESSED_PATTERN, compressed_pattern);
+ }
+ explicit PatternBuilder(flatbuffers::FlatBufferBuilder &_fbb)
+ : fbb_(_fbb) {
+ start_ = fbb_.StartTable();
+ }
+ PatternBuilder &operator=(const PatternBuilder &);
+ flatbuffers::Offset<Pattern> Finish() {
+ const auto end = fbb_.EndTable(start_);
+ auto o = flatbuffers::Offset<Pattern>(end);
+ return o;
+ }
+};
+
+inline flatbuffers::Offset<Pattern> CreatePattern(
+ flatbuffers::FlatBufferBuilder &_fbb,
+ flatbuffers::Offset<flatbuffers::String> collection_name = 0,
+ flatbuffers::Offset<flatbuffers::String> pattern = 0,
+ libtextclassifier2::ModeFlag enabled_modes = libtextclassifier2::ModeFlag_ALL,
+ float target_classification_score = 1.0f,
+ float priority_score = 0.0f,
+ bool use_approximate_matching = false,
+ flatbuffers::Offset<libtextclassifier2::CompressedBuffer> compressed_pattern = 0) {
+ PatternBuilder builder_(_fbb);
+ builder_.add_compressed_pattern(compressed_pattern);
+ builder_.add_priority_score(priority_score);
+ builder_.add_target_classification_score(target_classification_score);
+ builder_.add_enabled_modes(enabled_modes);
+ builder_.add_pattern(pattern);
+ builder_.add_collection_name(collection_name);
+ builder_.add_use_approximate_matching(use_approximate_matching);
+ return builder_.Finish();
+}
+
+inline flatbuffers::Offset<Pattern> CreatePatternDirect(
+ flatbuffers::FlatBufferBuilder &_fbb,
+ const char *collection_name = nullptr,
+ const char *pattern = nullptr,
+ libtextclassifier2::ModeFlag enabled_modes = libtextclassifier2::ModeFlag_ALL,
+ float target_classification_score = 1.0f,
+ float priority_score = 0.0f,
+ bool use_approximate_matching = false,
+ flatbuffers::Offset<libtextclassifier2::CompressedBuffer> compressed_pattern = 0) {
+ return libtextclassifier2::RegexModel_::CreatePattern(
+ _fbb,
+ collection_name ? _fbb.CreateString(collection_name) : 0,
+ pattern ? _fbb.CreateString(pattern) : 0,
+ enabled_modes,
+ target_classification_score,
+ priority_score,
+ use_approximate_matching,
+ compressed_pattern);
+}
+
+flatbuffers::Offset<Pattern> CreatePattern(flatbuffers::FlatBufferBuilder &_fbb, const PatternT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
+
+} // namespace RegexModel_
+
+struct RegexModelT : public flatbuffers::NativeTable {
+ typedef RegexModel TableType;
+ std::vector<std::unique_ptr<libtextclassifier2::RegexModel_::PatternT>> patterns;
+ RegexModelT() {
+ }
+};
+
+struct RegexModel FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table {
+ typedef RegexModelT NativeTableType;
+ enum {
+ VT_PATTERNS = 4
+ };
+ const flatbuffers::Vector<flatbuffers::Offset<libtextclassifier2::RegexModel_::Pattern>> *patterns() const {
+ return GetPointer<const flatbuffers::Vector<flatbuffers::Offset<libtextclassifier2::RegexModel_::Pattern>> *>(VT_PATTERNS);
+ }
+ bool Verify(flatbuffers::Verifier &verifier) const {
+ return VerifyTableStart(verifier) &&
+ VerifyOffset(verifier, VT_PATTERNS) &&
+ verifier.Verify(patterns()) &&
+ verifier.VerifyVectorOfTables(patterns()) &&
+ verifier.EndTable();
+ }
+ RegexModelT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ void UnPackTo(RegexModelT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ static flatbuffers::Offset<RegexModel> Pack(flatbuffers::FlatBufferBuilder &_fbb, const RegexModelT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
+};
+
+struct RegexModelBuilder {
+ flatbuffers::FlatBufferBuilder &fbb_;
+ flatbuffers::uoffset_t start_;
+ void add_patterns(flatbuffers::Offset<flatbuffers::Vector<flatbuffers::Offset<libtextclassifier2::RegexModel_::Pattern>>> patterns) {
+ fbb_.AddOffset(RegexModel::VT_PATTERNS, patterns);
+ }
+ explicit RegexModelBuilder(flatbuffers::FlatBufferBuilder &_fbb)
+ : fbb_(_fbb) {
+ start_ = fbb_.StartTable();
+ }
+ RegexModelBuilder &operator=(const RegexModelBuilder &);
+ flatbuffers::Offset<RegexModel> Finish() {
+ const auto end = fbb_.EndTable(start_);
+ auto o = flatbuffers::Offset<RegexModel>(end);
+ return o;
+ }
+};
+
+inline flatbuffers::Offset<RegexModel> CreateRegexModel(
+ flatbuffers::FlatBufferBuilder &_fbb,
+ flatbuffers::Offset<flatbuffers::Vector<flatbuffers::Offset<libtextclassifier2::RegexModel_::Pattern>>> patterns = 0) {
+ RegexModelBuilder builder_(_fbb);
+ builder_.add_patterns(patterns);
+ return builder_.Finish();
+}
+
+inline flatbuffers::Offset<RegexModel> CreateRegexModelDirect(
+ flatbuffers::FlatBufferBuilder &_fbb,
+ const std::vector<flatbuffers::Offset<libtextclassifier2::RegexModel_::Pattern>> *patterns = nullptr) {
+ return libtextclassifier2::CreateRegexModel(
+ _fbb,
+ patterns ? _fbb.CreateVector<flatbuffers::Offset<libtextclassifier2::RegexModel_::Pattern>>(*patterns) : 0);
+}
+
+flatbuffers::Offset<RegexModel> CreateRegexModel(flatbuffers::FlatBufferBuilder &_fbb, const RegexModelT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
+
+namespace DatetimeModelPattern_ {
+
+struct RegexT : public flatbuffers::NativeTable {
+ typedef Regex TableType;
+ std::string pattern;
+ std::vector<libtextclassifier2::DatetimeGroupType> groups;
+ std::unique_ptr<libtextclassifier2::CompressedBufferT> compressed_pattern;
+ RegexT() {
+ }
+};
+
+struct Regex FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table {
+ typedef RegexT NativeTableType;
+ enum {
+ VT_PATTERN = 4,
+ VT_GROUPS = 6,
+ VT_COMPRESSED_PATTERN = 8
+ };
+ const flatbuffers::String *pattern() const {
+ return GetPointer<const flatbuffers::String *>(VT_PATTERN);
+ }
+ const flatbuffers::Vector<int32_t> *groups() const {
+ return GetPointer<const flatbuffers::Vector<int32_t> *>(VT_GROUPS);
+ }
+ const libtextclassifier2::CompressedBuffer *compressed_pattern() const {
+ return GetPointer<const libtextclassifier2::CompressedBuffer *>(VT_COMPRESSED_PATTERN);
+ }
+ bool Verify(flatbuffers::Verifier &verifier) const {
+ return VerifyTableStart(verifier) &&
+ VerifyOffset(verifier, VT_PATTERN) &&
+ verifier.Verify(pattern()) &&
+ VerifyOffset(verifier, VT_GROUPS) &&
+ verifier.Verify(groups()) &&
+ VerifyOffset(verifier, VT_COMPRESSED_PATTERN) &&
+ verifier.VerifyTable(compressed_pattern()) &&
+ verifier.EndTable();
+ }
+ RegexT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ void UnPackTo(RegexT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ static flatbuffers::Offset<Regex> Pack(flatbuffers::FlatBufferBuilder &_fbb, const RegexT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
+};
+
+struct RegexBuilder {
+ flatbuffers::FlatBufferBuilder &fbb_;
+ flatbuffers::uoffset_t start_;
+ void add_pattern(flatbuffers::Offset<flatbuffers::String> pattern) {
+ fbb_.AddOffset(Regex::VT_PATTERN, pattern);
+ }
+ void add_groups(flatbuffers::Offset<flatbuffers::Vector<int32_t>> groups) {
+ fbb_.AddOffset(Regex::VT_GROUPS, groups);
+ }
+ void add_compressed_pattern(flatbuffers::Offset<libtextclassifier2::CompressedBuffer> compressed_pattern) {
+ fbb_.AddOffset(Regex::VT_COMPRESSED_PATTERN, compressed_pattern);
+ }
+ explicit RegexBuilder(flatbuffers::FlatBufferBuilder &_fbb)
+ : fbb_(_fbb) {
+ start_ = fbb_.StartTable();
+ }
+ RegexBuilder &operator=(const RegexBuilder &);
+ flatbuffers::Offset<Regex> Finish() {
+ const auto end = fbb_.EndTable(start_);
+ auto o = flatbuffers::Offset<Regex>(end);
+ return o;
+ }
+};
+
+inline flatbuffers::Offset<Regex> CreateRegex(
+ flatbuffers::FlatBufferBuilder &_fbb,
+ flatbuffers::Offset<flatbuffers::String> pattern = 0,
+ flatbuffers::Offset<flatbuffers::Vector<int32_t>> groups = 0,
+ flatbuffers::Offset<libtextclassifier2::CompressedBuffer> compressed_pattern = 0) {
+ RegexBuilder builder_(_fbb);
+ builder_.add_compressed_pattern(compressed_pattern);
+ builder_.add_groups(groups);
+ builder_.add_pattern(pattern);
+ return builder_.Finish();
+}
+
+inline flatbuffers::Offset<Regex> CreateRegexDirect(
+ flatbuffers::FlatBufferBuilder &_fbb,
+ const char *pattern = nullptr,
+ const std::vector<int32_t> *groups = nullptr,
+ flatbuffers::Offset<libtextclassifier2::CompressedBuffer> compressed_pattern = 0) {
+ return libtextclassifier2::DatetimeModelPattern_::CreateRegex(
+ _fbb,
+ pattern ? _fbb.CreateString(pattern) : 0,
+ groups ? _fbb.CreateVector<int32_t>(*groups) : 0,
+ compressed_pattern);
+}
+
+flatbuffers::Offset<Regex> CreateRegex(flatbuffers::FlatBufferBuilder &_fbb, const RegexT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
+
+} // namespace DatetimeModelPattern_
+
+struct DatetimeModelPatternT : public flatbuffers::NativeTable {
+ typedef DatetimeModelPattern TableType;
+ std::vector<std::unique_ptr<libtextclassifier2::DatetimeModelPattern_::RegexT>> regexes;
+ std::vector<int32_t> locales;
+ float target_classification_score;
+ float priority_score;
+ ModeFlag enabled_modes;
+ DatetimeModelPatternT()
+ : target_classification_score(1.0f),
+ priority_score(0.0f),
+ enabled_modes(ModeFlag_ALL) {
+ }
+};
+
+struct DatetimeModelPattern FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table {
+ typedef DatetimeModelPatternT NativeTableType;
+ enum {
+ VT_REGEXES = 4,
+ VT_LOCALES = 6,
+ VT_TARGET_CLASSIFICATION_SCORE = 8,
+ VT_PRIORITY_SCORE = 10,
+ VT_ENABLED_MODES = 12
+ };
+ const flatbuffers::Vector<flatbuffers::Offset<libtextclassifier2::DatetimeModelPattern_::Regex>> *regexes() const {
+ return GetPointer<const flatbuffers::Vector<flatbuffers::Offset<libtextclassifier2::DatetimeModelPattern_::Regex>> *>(VT_REGEXES);
+ }
+ const flatbuffers::Vector<int32_t> *locales() const {
+ return GetPointer<const flatbuffers::Vector<int32_t> *>(VT_LOCALES);
+ }
+ float target_classification_score() const {
+ return GetField<float>(VT_TARGET_CLASSIFICATION_SCORE, 1.0f);
+ }
+ float priority_score() const {
+ return GetField<float>(VT_PRIORITY_SCORE, 0.0f);
+ }
+ ModeFlag enabled_modes() const {
+ return static_cast<ModeFlag>(GetField<int32_t>(VT_ENABLED_MODES, 7));
+ }
+ bool Verify(flatbuffers::Verifier &verifier) const {
+ return VerifyTableStart(verifier) &&
+ VerifyOffset(verifier, VT_REGEXES) &&
+ verifier.Verify(regexes()) &&
+ verifier.VerifyVectorOfTables(regexes()) &&
+ VerifyOffset(verifier, VT_LOCALES) &&
+ verifier.Verify(locales()) &&
+ VerifyField<float>(verifier, VT_TARGET_CLASSIFICATION_SCORE) &&
+ VerifyField<float>(verifier, VT_PRIORITY_SCORE) &&
+ VerifyField<int32_t>(verifier, VT_ENABLED_MODES) &&
+ verifier.EndTable();
+ }
+ DatetimeModelPatternT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ void UnPackTo(DatetimeModelPatternT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ static flatbuffers::Offset<DatetimeModelPattern> Pack(flatbuffers::FlatBufferBuilder &_fbb, const DatetimeModelPatternT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
+};
+
+struct DatetimeModelPatternBuilder {
+ flatbuffers::FlatBufferBuilder &fbb_;
+ flatbuffers::uoffset_t start_;
+ void add_regexes(flatbuffers::Offset<flatbuffers::Vector<flatbuffers::Offset<libtextclassifier2::DatetimeModelPattern_::Regex>>> regexes) {
+ fbb_.AddOffset(DatetimeModelPattern::VT_REGEXES, regexes);
+ }
+ void add_locales(flatbuffers::Offset<flatbuffers::Vector<int32_t>> locales) {
+ fbb_.AddOffset(DatetimeModelPattern::VT_LOCALES, locales);
+ }
+ void add_target_classification_score(float target_classification_score) {
+ fbb_.AddElement<float>(DatetimeModelPattern::VT_TARGET_CLASSIFICATION_SCORE, target_classification_score, 1.0f);
+ }
+ void add_priority_score(float priority_score) {
+ fbb_.AddElement<float>(DatetimeModelPattern::VT_PRIORITY_SCORE, priority_score, 0.0f);
+ }
+ void add_enabled_modes(ModeFlag enabled_modes) {
+ fbb_.AddElement<int32_t>(DatetimeModelPattern::VT_ENABLED_MODES, static_cast<int32_t>(enabled_modes), 7);
+ }
+ explicit DatetimeModelPatternBuilder(flatbuffers::FlatBufferBuilder &_fbb)
+ : fbb_(_fbb) {
+ start_ = fbb_.StartTable();
+ }
+ DatetimeModelPatternBuilder &operator=(const DatetimeModelPatternBuilder &);
+ flatbuffers::Offset<DatetimeModelPattern> Finish() {
+ const auto end = fbb_.EndTable(start_);
+ auto o = flatbuffers::Offset<DatetimeModelPattern>(end);
+ return o;
+ }
+};
+
+inline flatbuffers::Offset<DatetimeModelPattern> CreateDatetimeModelPattern(
+ flatbuffers::FlatBufferBuilder &_fbb,
+ flatbuffers::Offset<flatbuffers::Vector<flatbuffers::Offset<libtextclassifier2::DatetimeModelPattern_::Regex>>> regexes = 0,
+ flatbuffers::Offset<flatbuffers::Vector<int32_t>> locales = 0,
+ float target_classification_score = 1.0f,
+ float priority_score = 0.0f,
+ ModeFlag enabled_modes = ModeFlag_ALL) {
+ DatetimeModelPatternBuilder builder_(_fbb);
+ builder_.add_enabled_modes(enabled_modes);
+ builder_.add_priority_score(priority_score);
+ builder_.add_target_classification_score(target_classification_score);
+ builder_.add_locales(locales);
+ builder_.add_regexes(regexes);
+ return builder_.Finish();
+}
+
+inline flatbuffers::Offset<DatetimeModelPattern> CreateDatetimeModelPatternDirect(
+ flatbuffers::FlatBufferBuilder &_fbb,
+ const std::vector<flatbuffers::Offset<libtextclassifier2::DatetimeModelPattern_::Regex>> *regexes = nullptr,
+ const std::vector<int32_t> *locales = nullptr,
+ float target_classification_score = 1.0f,
+ float priority_score = 0.0f,
+ ModeFlag enabled_modes = ModeFlag_ALL) {
+ return libtextclassifier2::CreateDatetimeModelPattern(
+ _fbb,
+ regexes ? _fbb.CreateVector<flatbuffers::Offset<libtextclassifier2::DatetimeModelPattern_::Regex>>(*regexes) : 0,
+ locales ? _fbb.CreateVector<int32_t>(*locales) : 0,
+ target_classification_score,
+ priority_score,
+ enabled_modes);
+}
+
+flatbuffers::Offset<DatetimeModelPattern> CreateDatetimeModelPattern(flatbuffers::FlatBufferBuilder &_fbb, const DatetimeModelPatternT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
+
+struct DatetimeModelExtractorT : public flatbuffers::NativeTable {
+ typedef DatetimeModelExtractor TableType;
+ DatetimeExtractorType extractor;
+ std::string pattern;
+ std::vector<int32_t> locales;
+ std::unique_ptr<CompressedBufferT> compressed_pattern;
+ DatetimeModelExtractorT()
+ : extractor(DatetimeExtractorType_UNKNOWN_DATETIME_EXTRACTOR_TYPE) {
+ }
+};
+
+struct DatetimeModelExtractor FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table {
+ typedef DatetimeModelExtractorT NativeTableType;
+ enum {
+ VT_EXTRACTOR = 4,
+ VT_PATTERN = 6,
+ VT_LOCALES = 8,
+ VT_COMPRESSED_PATTERN = 10
+ };
+ DatetimeExtractorType extractor() const {
+ return static_cast<DatetimeExtractorType>(GetField<int32_t>(VT_EXTRACTOR, 0));
+ }
+ const flatbuffers::String *pattern() const {
+ return GetPointer<const flatbuffers::String *>(VT_PATTERN);
+ }
+ const flatbuffers::Vector<int32_t> *locales() const {
+ return GetPointer<const flatbuffers::Vector<int32_t> *>(VT_LOCALES);
+ }
+ const CompressedBuffer *compressed_pattern() const {
+ return GetPointer<const CompressedBuffer *>(VT_COMPRESSED_PATTERN);
+ }
+ bool Verify(flatbuffers::Verifier &verifier) const {
+ return VerifyTableStart(verifier) &&
+ VerifyField<int32_t>(verifier, VT_EXTRACTOR) &&
+ VerifyOffset(verifier, VT_PATTERN) &&
+ verifier.Verify(pattern()) &&
+ VerifyOffset(verifier, VT_LOCALES) &&
+ verifier.Verify(locales()) &&
+ VerifyOffset(verifier, VT_COMPRESSED_PATTERN) &&
+ verifier.VerifyTable(compressed_pattern()) &&
+ verifier.EndTable();
+ }
+ DatetimeModelExtractorT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ void UnPackTo(DatetimeModelExtractorT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ static flatbuffers::Offset<DatetimeModelExtractor> Pack(flatbuffers::FlatBufferBuilder &_fbb, const DatetimeModelExtractorT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
+};
+
+struct DatetimeModelExtractorBuilder {
+ flatbuffers::FlatBufferBuilder &fbb_;
+ flatbuffers::uoffset_t start_;
+ void add_extractor(DatetimeExtractorType extractor) {
+ fbb_.AddElement<int32_t>(DatetimeModelExtractor::VT_EXTRACTOR, static_cast<int32_t>(extractor), 0);
+ }
+ void add_pattern(flatbuffers::Offset<flatbuffers::String> pattern) {
+ fbb_.AddOffset(DatetimeModelExtractor::VT_PATTERN, pattern);
+ }
+ void add_locales(flatbuffers::Offset<flatbuffers::Vector<int32_t>> locales) {
+ fbb_.AddOffset(DatetimeModelExtractor::VT_LOCALES, locales);
+ }
+ void add_compressed_pattern(flatbuffers::Offset<CompressedBuffer> compressed_pattern) {
+ fbb_.AddOffset(DatetimeModelExtractor::VT_COMPRESSED_PATTERN, compressed_pattern);
+ }
+ explicit DatetimeModelExtractorBuilder(flatbuffers::FlatBufferBuilder &_fbb)
+ : fbb_(_fbb) {
+ start_ = fbb_.StartTable();
+ }
+ DatetimeModelExtractorBuilder &operator=(const DatetimeModelExtractorBuilder &);
+ flatbuffers::Offset<DatetimeModelExtractor> Finish() {
+ const auto end = fbb_.EndTable(start_);
+ auto o = flatbuffers::Offset<DatetimeModelExtractor>(end);
+ return o;
+ }
+};
+
+inline flatbuffers::Offset<DatetimeModelExtractor> CreateDatetimeModelExtractor(
+ flatbuffers::FlatBufferBuilder &_fbb,
+ DatetimeExtractorType extractor = DatetimeExtractorType_UNKNOWN_DATETIME_EXTRACTOR_TYPE,
+ flatbuffers::Offset<flatbuffers::String> pattern = 0,
+ flatbuffers::Offset<flatbuffers::Vector<int32_t>> locales = 0,
+ flatbuffers::Offset<CompressedBuffer> compressed_pattern = 0) {
+ DatetimeModelExtractorBuilder builder_(_fbb);
+ builder_.add_compressed_pattern(compressed_pattern);
+ builder_.add_locales(locales);
+ builder_.add_pattern(pattern);
+ builder_.add_extractor(extractor);
+ return builder_.Finish();
+}
+
+inline flatbuffers::Offset<DatetimeModelExtractor> CreateDatetimeModelExtractorDirect(
+ flatbuffers::FlatBufferBuilder &_fbb,
+ DatetimeExtractorType extractor = DatetimeExtractorType_UNKNOWN_DATETIME_EXTRACTOR_TYPE,
+ const char *pattern = nullptr,
+ const std::vector<int32_t> *locales = nullptr,
+ flatbuffers::Offset<CompressedBuffer> compressed_pattern = 0) {
+ return libtextclassifier2::CreateDatetimeModelExtractor(
+ _fbb,
+ extractor,
+ pattern ? _fbb.CreateString(pattern) : 0,
+ locales ? _fbb.CreateVector<int32_t>(*locales) : 0,
+ compressed_pattern);
+}
+
+flatbuffers::Offset<DatetimeModelExtractor> CreateDatetimeModelExtractor(flatbuffers::FlatBufferBuilder &_fbb, const DatetimeModelExtractorT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
+
+struct DatetimeModelT : public flatbuffers::NativeTable {
+ typedef DatetimeModel TableType;
+ std::vector<std::string> locales;
+ std::vector<std::unique_ptr<DatetimeModelPatternT>> patterns;
+ std::vector<std::unique_ptr<DatetimeModelExtractorT>> extractors;
+ bool use_extractors_for_locating;
+ std::vector<int32_t> default_locales;
+ DatetimeModelT()
+ : use_extractors_for_locating(true) {
+ }
+};
+
+struct DatetimeModel FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table {
+ typedef DatetimeModelT NativeTableType;
+ enum {
+ VT_LOCALES = 4,
+ VT_PATTERNS = 6,
+ VT_EXTRACTORS = 8,
+ VT_USE_EXTRACTORS_FOR_LOCATING = 10,
+ VT_DEFAULT_LOCALES = 12
+ };
+ const flatbuffers::Vector<flatbuffers::Offset<flatbuffers::String>> *locales() const {
+ return GetPointer<const flatbuffers::Vector<flatbuffers::Offset<flatbuffers::String>> *>(VT_LOCALES);
+ }
+ const flatbuffers::Vector<flatbuffers::Offset<DatetimeModelPattern>> *patterns() const {
+ return GetPointer<const flatbuffers::Vector<flatbuffers::Offset<DatetimeModelPattern>> *>(VT_PATTERNS);
+ }
+ const flatbuffers::Vector<flatbuffers::Offset<DatetimeModelExtractor>> *extractors() const {
+ return GetPointer<const flatbuffers::Vector<flatbuffers::Offset<DatetimeModelExtractor>> *>(VT_EXTRACTORS);
+ }
+ bool use_extractors_for_locating() const {
+ return GetField<uint8_t>(VT_USE_EXTRACTORS_FOR_LOCATING, 1) != 0;
+ }
+ const flatbuffers::Vector<int32_t> *default_locales() const {
+ return GetPointer<const flatbuffers::Vector<int32_t> *>(VT_DEFAULT_LOCALES);
+ }
+ bool Verify(flatbuffers::Verifier &verifier) const {
+ return VerifyTableStart(verifier) &&
+ VerifyOffset(verifier, VT_LOCALES) &&
+ verifier.Verify(locales()) &&
+ verifier.VerifyVectorOfStrings(locales()) &&
+ VerifyOffset(verifier, VT_PATTERNS) &&
+ verifier.Verify(patterns()) &&
+ verifier.VerifyVectorOfTables(patterns()) &&
+ VerifyOffset(verifier, VT_EXTRACTORS) &&
+ verifier.Verify(extractors()) &&
+ verifier.VerifyVectorOfTables(extractors()) &&
+ VerifyField<uint8_t>(verifier, VT_USE_EXTRACTORS_FOR_LOCATING) &&
+ VerifyOffset(verifier, VT_DEFAULT_LOCALES) &&
+ verifier.Verify(default_locales()) &&
+ verifier.EndTable();
+ }
+ DatetimeModelT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ void UnPackTo(DatetimeModelT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ static flatbuffers::Offset<DatetimeModel> Pack(flatbuffers::FlatBufferBuilder &_fbb, const DatetimeModelT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
+};
+
+struct DatetimeModelBuilder {
+ flatbuffers::FlatBufferBuilder &fbb_;
+ flatbuffers::uoffset_t start_;
+ void add_locales(flatbuffers::Offset<flatbuffers::Vector<flatbuffers::Offset<flatbuffers::String>>> locales) {
+ fbb_.AddOffset(DatetimeModel::VT_LOCALES, locales);
+ }
+ void add_patterns(flatbuffers::Offset<flatbuffers::Vector<flatbuffers::Offset<DatetimeModelPattern>>> patterns) {
+ fbb_.AddOffset(DatetimeModel::VT_PATTERNS, patterns);
+ }
+ void add_extractors(flatbuffers::Offset<flatbuffers::Vector<flatbuffers::Offset<DatetimeModelExtractor>>> extractors) {
+ fbb_.AddOffset(DatetimeModel::VT_EXTRACTORS, extractors);
+ }
+ void add_use_extractors_for_locating(bool use_extractors_for_locating) {
+ fbb_.AddElement<uint8_t>(DatetimeModel::VT_USE_EXTRACTORS_FOR_LOCATING, static_cast<uint8_t>(use_extractors_for_locating), 1);
+ }
+ void add_default_locales(flatbuffers::Offset<flatbuffers::Vector<int32_t>> default_locales) {
+ fbb_.AddOffset(DatetimeModel::VT_DEFAULT_LOCALES, default_locales);
+ }
+ explicit DatetimeModelBuilder(flatbuffers::FlatBufferBuilder &_fbb)
+ : fbb_(_fbb) {
+ start_ = fbb_.StartTable();
+ }
+ DatetimeModelBuilder &operator=(const DatetimeModelBuilder &);
+ flatbuffers::Offset<DatetimeModel> Finish() {
+ const auto end = fbb_.EndTable(start_);
+ auto o = flatbuffers::Offset<DatetimeModel>(end);
+ return o;
+ }
+};
+
+inline flatbuffers::Offset<DatetimeModel> CreateDatetimeModel(
+ flatbuffers::FlatBufferBuilder &_fbb,
+ flatbuffers::Offset<flatbuffers::Vector<flatbuffers::Offset<flatbuffers::String>>> locales = 0,
+ flatbuffers::Offset<flatbuffers::Vector<flatbuffers::Offset<DatetimeModelPattern>>> patterns = 0,
+ flatbuffers::Offset<flatbuffers::Vector<flatbuffers::Offset<DatetimeModelExtractor>>> extractors = 0,
+ bool use_extractors_for_locating = true,
+ flatbuffers::Offset<flatbuffers::Vector<int32_t>> default_locales = 0) {
+ DatetimeModelBuilder builder_(_fbb);
+ builder_.add_default_locales(default_locales);
+ builder_.add_extractors(extractors);
+ builder_.add_patterns(patterns);
+ builder_.add_locales(locales);
+ builder_.add_use_extractors_for_locating(use_extractors_for_locating);
+ return builder_.Finish();
+}
+
+inline flatbuffers::Offset<DatetimeModel> CreateDatetimeModelDirect(
+ flatbuffers::FlatBufferBuilder &_fbb,
+ const std::vector<flatbuffers::Offset<flatbuffers::String>> *locales = nullptr,
+ const std::vector<flatbuffers::Offset<DatetimeModelPattern>> *patterns = nullptr,
+ const std::vector<flatbuffers::Offset<DatetimeModelExtractor>> *extractors = nullptr,
+ bool use_extractors_for_locating = true,
+ const std::vector<int32_t> *default_locales = nullptr) {
+ return libtextclassifier2::CreateDatetimeModel(
+ _fbb,
+ locales ? _fbb.CreateVector<flatbuffers::Offset<flatbuffers::String>>(*locales) : 0,
+ patterns ? _fbb.CreateVector<flatbuffers::Offset<DatetimeModelPattern>>(*patterns) : 0,
+ extractors ? _fbb.CreateVector<flatbuffers::Offset<DatetimeModelExtractor>>(*extractors) : 0,
+ use_extractors_for_locating,
+ default_locales ? _fbb.CreateVector<int32_t>(*default_locales) : 0);
+}
+
+flatbuffers::Offset<DatetimeModel> CreateDatetimeModel(flatbuffers::FlatBufferBuilder &_fbb, const DatetimeModelT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
+
+namespace DatetimeModelLibrary_ {
+
+struct ItemT : public flatbuffers::NativeTable {
+ typedef Item TableType;
+ std::string key;
+ std::unique_ptr<libtextclassifier2::DatetimeModelT> value;
+ ItemT() {
+ }
+};
+
+struct Item FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table {
+ typedef ItemT NativeTableType;
+ enum {
+ VT_KEY = 4,
+ VT_VALUE = 6
+ };
+ const flatbuffers::String *key() const {
+ return GetPointer<const flatbuffers::String *>(VT_KEY);
+ }
+ const libtextclassifier2::DatetimeModel *value() const {
+ return GetPointer<const libtextclassifier2::DatetimeModel *>(VT_VALUE);
+ }
+ bool Verify(flatbuffers::Verifier &verifier) const {
+ return VerifyTableStart(verifier) &&
+ VerifyOffset(verifier, VT_KEY) &&
+ verifier.Verify(key()) &&
+ VerifyOffset(verifier, VT_VALUE) &&
+ verifier.VerifyTable(value()) &&
+ verifier.EndTable();
+ }
+ ItemT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ void UnPackTo(ItemT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ static flatbuffers::Offset<Item> Pack(flatbuffers::FlatBufferBuilder &_fbb, const ItemT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
+};
+
+struct ItemBuilder {
+ flatbuffers::FlatBufferBuilder &fbb_;
+ flatbuffers::uoffset_t start_;
+ void add_key(flatbuffers::Offset<flatbuffers::String> key) {
+ fbb_.AddOffset(Item::VT_KEY, key);
+ }
+ void add_value(flatbuffers::Offset<libtextclassifier2::DatetimeModel> value) {
+ fbb_.AddOffset(Item::VT_VALUE, value);
+ }
+ explicit ItemBuilder(flatbuffers::FlatBufferBuilder &_fbb)
+ : fbb_(_fbb) {
+ start_ = fbb_.StartTable();
+ }
+ ItemBuilder &operator=(const ItemBuilder &);
+ flatbuffers::Offset<Item> Finish() {
+ const auto end = fbb_.EndTable(start_);
+ auto o = flatbuffers::Offset<Item>(end);
+ return o;
+ }
+};
+
+inline flatbuffers::Offset<Item> CreateItem(
+ flatbuffers::FlatBufferBuilder &_fbb,
+ flatbuffers::Offset<flatbuffers::String> key = 0,
+ flatbuffers::Offset<libtextclassifier2::DatetimeModel> value = 0) {
+ ItemBuilder builder_(_fbb);
+ builder_.add_value(value);
+ builder_.add_key(key);
+ return builder_.Finish();
+}
+
+inline flatbuffers::Offset<Item> CreateItemDirect(
+ flatbuffers::FlatBufferBuilder &_fbb,
+ const char *key = nullptr,
+ flatbuffers::Offset<libtextclassifier2::DatetimeModel> value = 0) {
+ return libtextclassifier2::DatetimeModelLibrary_::CreateItem(
+ _fbb,
+ key ? _fbb.CreateString(key) : 0,
+ value);
+}
+
+flatbuffers::Offset<Item> CreateItem(flatbuffers::FlatBufferBuilder &_fbb, const ItemT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
+
+} // namespace DatetimeModelLibrary_
+
+struct DatetimeModelLibraryT : public flatbuffers::NativeTable {
+ typedef DatetimeModelLibrary TableType;
+ std::vector<std::unique_ptr<libtextclassifier2::DatetimeModelLibrary_::ItemT>> models;
+ DatetimeModelLibraryT() {
+ }
+};
+
+struct DatetimeModelLibrary FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table {
+ typedef DatetimeModelLibraryT NativeTableType;
+ enum {
+ VT_MODELS = 4
+ };
+ const flatbuffers::Vector<flatbuffers::Offset<libtextclassifier2::DatetimeModelLibrary_::Item>> *models() const {
+ return GetPointer<const flatbuffers::Vector<flatbuffers::Offset<libtextclassifier2::DatetimeModelLibrary_::Item>> *>(VT_MODELS);
+ }
+ bool Verify(flatbuffers::Verifier &verifier) const {
+ return VerifyTableStart(verifier) &&
+ VerifyOffset(verifier, VT_MODELS) &&
+ verifier.Verify(models()) &&
+ verifier.VerifyVectorOfTables(models()) &&
+ verifier.EndTable();
+ }
+ DatetimeModelLibraryT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ void UnPackTo(DatetimeModelLibraryT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ static flatbuffers::Offset<DatetimeModelLibrary> Pack(flatbuffers::FlatBufferBuilder &_fbb, const DatetimeModelLibraryT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
+};
+
+struct DatetimeModelLibraryBuilder {
+ flatbuffers::FlatBufferBuilder &fbb_;
+ flatbuffers::uoffset_t start_;
+ void add_models(flatbuffers::Offset<flatbuffers::Vector<flatbuffers::Offset<libtextclassifier2::DatetimeModelLibrary_::Item>>> models) {
+ fbb_.AddOffset(DatetimeModelLibrary::VT_MODELS, models);
+ }
+ explicit DatetimeModelLibraryBuilder(flatbuffers::FlatBufferBuilder &_fbb)
+ : fbb_(_fbb) {
+ start_ = fbb_.StartTable();
+ }
+ DatetimeModelLibraryBuilder &operator=(const DatetimeModelLibraryBuilder &);
+ flatbuffers::Offset<DatetimeModelLibrary> Finish() {
+ const auto end = fbb_.EndTable(start_);
+ auto o = flatbuffers::Offset<DatetimeModelLibrary>(end);
+ return o;
+ }
+};
+
+inline flatbuffers::Offset<DatetimeModelLibrary> CreateDatetimeModelLibrary(
+ flatbuffers::FlatBufferBuilder &_fbb,
+ flatbuffers::Offset<flatbuffers::Vector<flatbuffers::Offset<libtextclassifier2::DatetimeModelLibrary_::Item>>> models = 0) {
+ DatetimeModelLibraryBuilder builder_(_fbb);
+ builder_.add_models(models);
+ return builder_.Finish();
+}
+
+inline flatbuffers::Offset<DatetimeModelLibrary> CreateDatetimeModelLibraryDirect(
+ flatbuffers::FlatBufferBuilder &_fbb,
+ const std::vector<flatbuffers::Offset<libtextclassifier2::DatetimeModelLibrary_::Item>> *models = nullptr) {
+ return libtextclassifier2::CreateDatetimeModelLibrary(
+ _fbb,
+ models ? _fbb.CreateVector<flatbuffers::Offset<libtextclassifier2::DatetimeModelLibrary_::Item>>(*models) : 0);
+}
+
+flatbuffers::Offset<DatetimeModelLibrary> CreateDatetimeModelLibrary(flatbuffers::FlatBufferBuilder &_fbb, const DatetimeModelLibraryT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
+
+struct ModelTriggeringOptionsT : public flatbuffers::NativeTable {
+ typedef ModelTriggeringOptions TableType;
+ float min_annotate_confidence;
+ ModeFlag enabled_modes;
+ ModelTriggeringOptionsT()
+ : min_annotate_confidence(0.0f),
+ enabled_modes(ModeFlag_ALL) {
+ }
+};
+
+struct ModelTriggeringOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table {
+ typedef ModelTriggeringOptionsT NativeTableType;
+ enum {
+ VT_MIN_ANNOTATE_CONFIDENCE = 4,
+ VT_ENABLED_MODES = 6
+ };
+ float min_annotate_confidence() const {
+ return GetField<float>(VT_MIN_ANNOTATE_CONFIDENCE, 0.0f);
+ }
+ ModeFlag enabled_modes() const {
+ return static_cast<ModeFlag>(GetField<int32_t>(VT_ENABLED_MODES, 7));
+ }
+ bool Verify(flatbuffers::Verifier &verifier) const {
+ return VerifyTableStart(verifier) &&
+ VerifyField<float>(verifier, VT_MIN_ANNOTATE_CONFIDENCE) &&
+ VerifyField<int32_t>(verifier, VT_ENABLED_MODES) &&
+ verifier.EndTable();
+ }
+ ModelTriggeringOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ void UnPackTo(ModelTriggeringOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ static flatbuffers::Offset<ModelTriggeringOptions> Pack(flatbuffers::FlatBufferBuilder &_fbb, const ModelTriggeringOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
+};
+
+struct ModelTriggeringOptionsBuilder {
+ flatbuffers::FlatBufferBuilder &fbb_;
+ flatbuffers::uoffset_t start_;
+ void add_min_annotate_confidence(float min_annotate_confidence) {
+ fbb_.AddElement<float>(ModelTriggeringOptions::VT_MIN_ANNOTATE_CONFIDENCE, min_annotate_confidence, 0.0f);
+ }
+ void add_enabled_modes(ModeFlag enabled_modes) {
+ fbb_.AddElement<int32_t>(ModelTriggeringOptions::VT_ENABLED_MODES, static_cast<int32_t>(enabled_modes), 7);
+ }
+ explicit ModelTriggeringOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb)
+ : fbb_(_fbb) {
+ start_ = fbb_.StartTable();
+ }
+ ModelTriggeringOptionsBuilder &operator=(const ModelTriggeringOptionsBuilder &);
+ flatbuffers::Offset<ModelTriggeringOptions> Finish() {
+ const auto end = fbb_.EndTable(start_);
+ auto o = flatbuffers::Offset<ModelTriggeringOptions>(end);
+ return o;
+ }
+};
+
+inline flatbuffers::Offset<ModelTriggeringOptions> CreateModelTriggeringOptions(
+ flatbuffers::FlatBufferBuilder &_fbb,
+ float min_annotate_confidence = 0.0f,
+ ModeFlag enabled_modes = ModeFlag_ALL) {
+ ModelTriggeringOptionsBuilder builder_(_fbb);
+ builder_.add_enabled_modes(enabled_modes);
+ builder_.add_min_annotate_confidence(min_annotate_confidence);
+ return builder_.Finish();
+}
+
+flatbuffers::Offset<ModelTriggeringOptions> CreateModelTriggeringOptions(flatbuffers::FlatBufferBuilder &_fbb, const ModelTriggeringOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
+
+struct OutputOptionsT : public flatbuffers::NativeTable {
+ typedef OutputOptions TableType;
+ std::vector<std::string> filtered_collections_annotation;
+ std::vector<std::string> filtered_collections_classification;
+ std::vector<std::string> filtered_collections_selection;
+ OutputOptionsT() {
+ }
+};
+
+struct OutputOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table {
+ typedef OutputOptionsT NativeTableType;
+ enum {
+ VT_FILTERED_COLLECTIONS_ANNOTATION = 4,
+ VT_FILTERED_COLLECTIONS_CLASSIFICATION = 6,
+ VT_FILTERED_COLLECTIONS_SELECTION = 8
+ };
+ const flatbuffers::Vector<flatbuffers::Offset<flatbuffers::String>> *filtered_collections_annotation() const {
+ return GetPointer<const flatbuffers::Vector<flatbuffers::Offset<flatbuffers::String>> *>(VT_FILTERED_COLLECTIONS_ANNOTATION);
+ }
+ const flatbuffers::Vector<flatbuffers::Offset<flatbuffers::String>> *filtered_collections_classification() const {
+ return GetPointer<const flatbuffers::Vector<flatbuffers::Offset<flatbuffers::String>> *>(VT_FILTERED_COLLECTIONS_CLASSIFICATION);
+ }
+ const flatbuffers::Vector<flatbuffers::Offset<flatbuffers::String>> *filtered_collections_selection() const {
+ return GetPointer<const flatbuffers::Vector<flatbuffers::Offset<flatbuffers::String>> *>(VT_FILTERED_COLLECTIONS_SELECTION);
+ }
+ bool Verify(flatbuffers::Verifier &verifier) const {
+ return VerifyTableStart(verifier) &&
+ VerifyOffset(verifier, VT_FILTERED_COLLECTIONS_ANNOTATION) &&
+ verifier.Verify(filtered_collections_annotation()) &&
+ verifier.VerifyVectorOfStrings(filtered_collections_annotation()) &&
+ VerifyOffset(verifier, VT_FILTERED_COLLECTIONS_CLASSIFICATION) &&
+ verifier.Verify(filtered_collections_classification()) &&
+ verifier.VerifyVectorOfStrings(filtered_collections_classification()) &&
+ VerifyOffset(verifier, VT_FILTERED_COLLECTIONS_SELECTION) &&
+ verifier.Verify(filtered_collections_selection()) &&
+ verifier.VerifyVectorOfStrings(filtered_collections_selection()) &&
+ verifier.EndTable();
+ }
+ OutputOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ void UnPackTo(OutputOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ static flatbuffers::Offset<OutputOptions> Pack(flatbuffers::FlatBufferBuilder &_fbb, const OutputOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
+};
+
+struct OutputOptionsBuilder {
+ flatbuffers::FlatBufferBuilder &fbb_;
+ flatbuffers::uoffset_t start_;
+ void add_filtered_collections_annotation(flatbuffers::Offset<flatbuffers::Vector<flatbuffers::Offset<flatbuffers::String>>> filtered_collections_annotation) {
+ fbb_.AddOffset(OutputOptions::VT_FILTERED_COLLECTIONS_ANNOTATION, filtered_collections_annotation);
+ }
+ void add_filtered_collections_classification(flatbuffers::Offset<flatbuffers::Vector<flatbuffers::Offset<flatbuffers::String>>> filtered_collections_classification) {
+ fbb_.AddOffset(OutputOptions::VT_FILTERED_COLLECTIONS_CLASSIFICATION, filtered_collections_classification);
+ }
+ void add_filtered_collections_selection(flatbuffers::Offset<flatbuffers::Vector<flatbuffers::Offset<flatbuffers::String>>> filtered_collections_selection) {
+ fbb_.AddOffset(OutputOptions::VT_FILTERED_COLLECTIONS_SELECTION, filtered_collections_selection);
+ }
+ explicit OutputOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb)
+ : fbb_(_fbb) {
+ start_ = fbb_.StartTable();
+ }
+ OutputOptionsBuilder &operator=(const OutputOptionsBuilder &);
+ flatbuffers::Offset<OutputOptions> Finish() {
+ const auto end = fbb_.EndTable(start_);
+ auto o = flatbuffers::Offset<OutputOptions>(end);
+ return o;
+ }
+};
+
+inline flatbuffers::Offset<OutputOptions> CreateOutputOptions(
+ flatbuffers::FlatBufferBuilder &_fbb,
+ flatbuffers::Offset<flatbuffers::Vector<flatbuffers::Offset<flatbuffers::String>>> filtered_collections_annotation = 0,
+ flatbuffers::Offset<flatbuffers::Vector<flatbuffers::Offset<flatbuffers::String>>> filtered_collections_classification = 0,
+ flatbuffers::Offset<flatbuffers::Vector<flatbuffers::Offset<flatbuffers::String>>> filtered_collections_selection = 0) {
+ OutputOptionsBuilder builder_(_fbb);
+ builder_.add_filtered_collections_selection(filtered_collections_selection);
+ builder_.add_filtered_collections_classification(filtered_collections_classification);
+ builder_.add_filtered_collections_annotation(filtered_collections_annotation);
+ return builder_.Finish();
+}
+
+inline flatbuffers::Offset<OutputOptions> CreateOutputOptionsDirect(
+ flatbuffers::FlatBufferBuilder &_fbb,
+ const std::vector<flatbuffers::Offset<flatbuffers::String>> *filtered_collections_annotation = nullptr,
+ const std::vector<flatbuffers::Offset<flatbuffers::String>> *filtered_collections_classification = nullptr,
+ const std::vector<flatbuffers::Offset<flatbuffers::String>> *filtered_collections_selection = nullptr) {
+ return libtextclassifier2::CreateOutputOptions(
+ _fbb,
+ filtered_collections_annotation ? _fbb.CreateVector<flatbuffers::Offset<flatbuffers::String>>(*filtered_collections_annotation) : 0,
+ filtered_collections_classification ? _fbb.CreateVector<flatbuffers::Offset<flatbuffers::String>>(*filtered_collections_classification) : 0,
+ filtered_collections_selection ? _fbb.CreateVector<flatbuffers::Offset<flatbuffers::String>>(*filtered_collections_selection) : 0);
+}
+
+flatbuffers::Offset<OutputOptions> CreateOutputOptions(flatbuffers::FlatBufferBuilder &_fbb, const OutputOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
+
+struct ModelT : public flatbuffers::NativeTable {
+ typedef Model TableType;
+ std::string locales;
+ int32_t version;
+ std::string name;
+ std::unique_ptr<FeatureProcessorOptionsT> selection_feature_options;
+ std::unique_ptr<FeatureProcessorOptionsT> classification_feature_options;
+ std::vector<uint8_t> selection_model;
+ std::vector<uint8_t> classification_model;
+ std::vector<uint8_t> embedding_model;
+ std::unique_ptr<SelectionModelOptionsT> selection_options;
+ std::unique_ptr<ClassificationModelOptionsT> classification_options;
+ std::unique_ptr<RegexModelT> regex_model;
+ std::unique_ptr<DatetimeModelT> datetime_model;
+ std::unique_ptr<ModelTriggeringOptionsT> triggering_options;
+ ModeFlag enabled_modes;
+ bool snap_whitespace_selections;
+ std::unique_ptr<OutputOptionsT> output_options;
+ ModelT()
+ : version(0),
+ enabled_modes(ModeFlag_ALL),
+ snap_whitespace_selections(true) {
+ }
+};
+
+struct Model FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table {
+ typedef ModelT NativeTableType;
+ enum {
+ VT_LOCALES = 4,
+ VT_VERSION = 6,
+ VT_NAME = 8,
+ VT_SELECTION_FEATURE_OPTIONS = 10,
+ VT_CLASSIFICATION_FEATURE_OPTIONS = 12,
+ VT_SELECTION_MODEL = 14,
+ VT_CLASSIFICATION_MODEL = 16,
+ VT_EMBEDDING_MODEL = 18,
+ VT_SELECTION_OPTIONS = 20,
+ VT_CLASSIFICATION_OPTIONS = 22,
+ VT_REGEX_MODEL = 24,
+ VT_DATETIME_MODEL = 26,
+ VT_TRIGGERING_OPTIONS = 28,
+ VT_ENABLED_MODES = 30,
+ VT_SNAP_WHITESPACE_SELECTIONS = 32,
+ VT_OUTPUT_OPTIONS = 34
+ };
+ const flatbuffers::String *locales() const {
+ return GetPointer<const flatbuffers::String *>(VT_LOCALES);
+ }
+ int32_t version() const {
+ return GetField<int32_t>(VT_VERSION, 0);
+ }
+ const flatbuffers::String *name() const {
+ return GetPointer<const flatbuffers::String *>(VT_NAME);
+ }
+ const FeatureProcessorOptions *selection_feature_options() const {
+ return GetPointer<const FeatureProcessorOptions *>(VT_SELECTION_FEATURE_OPTIONS);
+ }
+ const FeatureProcessorOptions *classification_feature_options() const {
+ return GetPointer<const FeatureProcessorOptions *>(VT_CLASSIFICATION_FEATURE_OPTIONS);
+ }
+ const flatbuffers::Vector<uint8_t> *selection_model() const {
+ return GetPointer<const flatbuffers::Vector<uint8_t> *>(VT_SELECTION_MODEL);
+ }
+ const flatbuffers::Vector<uint8_t> *classification_model() const {
+ return GetPointer<const flatbuffers::Vector<uint8_t> *>(VT_CLASSIFICATION_MODEL);
+ }
+ const flatbuffers::Vector<uint8_t> *embedding_model() const {
+ return GetPointer<const flatbuffers::Vector<uint8_t> *>(VT_EMBEDDING_MODEL);
+ }
+ const SelectionModelOptions *selection_options() const {
+ return GetPointer<const SelectionModelOptions *>(VT_SELECTION_OPTIONS);
+ }
+ const ClassificationModelOptions *classification_options() const {
+ return GetPointer<const ClassificationModelOptions *>(VT_CLASSIFICATION_OPTIONS);
+ }
+ const RegexModel *regex_model() const {
+ return GetPointer<const RegexModel *>(VT_REGEX_MODEL);
+ }
+ const DatetimeModel *datetime_model() const {
+ return GetPointer<const DatetimeModel *>(VT_DATETIME_MODEL);
+ }
+ const ModelTriggeringOptions *triggering_options() const {
+ return GetPointer<const ModelTriggeringOptions *>(VT_TRIGGERING_OPTIONS);
+ }
+ ModeFlag enabled_modes() const {
+ return static_cast<ModeFlag>(GetField<int32_t>(VT_ENABLED_MODES, 7));
+ }
+ bool snap_whitespace_selections() const {
+ return GetField<uint8_t>(VT_SNAP_WHITESPACE_SELECTIONS, 1) != 0;
+ }
+ const OutputOptions *output_options() const {
+ return GetPointer<const OutputOptions *>(VT_OUTPUT_OPTIONS);
+ }
+ bool Verify(flatbuffers::Verifier &verifier) const {
+ return VerifyTableStart(verifier) &&
+ VerifyOffset(verifier, VT_LOCALES) &&
+ verifier.Verify(locales()) &&
+ VerifyField<int32_t>(verifier, VT_VERSION) &&
+ VerifyOffset(verifier, VT_NAME) &&
+ verifier.Verify(name()) &&
+ VerifyOffset(verifier, VT_SELECTION_FEATURE_OPTIONS) &&
+ verifier.VerifyTable(selection_feature_options()) &&
+ VerifyOffset(verifier, VT_CLASSIFICATION_FEATURE_OPTIONS) &&
+ verifier.VerifyTable(classification_feature_options()) &&
+ VerifyOffset(verifier, VT_SELECTION_MODEL) &&
+ verifier.Verify(selection_model()) &&
+ VerifyOffset(verifier, VT_CLASSIFICATION_MODEL) &&
+ verifier.Verify(classification_model()) &&
+ VerifyOffset(verifier, VT_EMBEDDING_MODEL) &&
+ verifier.Verify(embedding_model()) &&
+ VerifyOffset(verifier, VT_SELECTION_OPTIONS) &&
+ verifier.VerifyTable(selection_options()) &&
+ VerifyOffset(verifier, VT_CLASSIFICATION_OPTIONS) &&
+ verifier.VerifyTable(classification_options()) &&
+ VerifyOffset(verifier, VT_REGEX_MODEL) &&
+ verifier.VerifyTable(regex_model()) &&
+ VerifyOffset(verifier, VT_DATETIME_MODEL) &&
+ verifier.VerifyTable(datetime_model()) &&
+ VerifyOffset(verifier, VT_TRIGGERING_OPTIONS) &&
+ verifier.VerifyTable(triggering_options()) &&
+ VerifyField<int32_t>(verifier, VT_ENABLED_MODES) &&
+ VerifyField<uint8_t>(verifier, VT_SNAP_WHITESPACE_SELECTIONS) &&
+ VerifyOffset(verifier, VT_OUTPUT_OPTIONS) &&
+ verifier.VerifyTable(output_options()) &&
+ verifier.EndTable();
+ }
+ ModelT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ void UnPackTo(ModelT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ static flatbuffers::Offset<Model> Pack(flatbuffers::FlatBufferBuilder &_fbb, const ModelT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
+};
+
+struct ModelBuilder {
+ flatbuffers::FlatBufferBuilder &fbb_;
+ flatbuffers::uoffset_t start_;
+ void add_locales(flatbuffers::Offset<flatbuffers::String> locales) {
+ fbb_.AddOffset(Model::VT_LOCALES, locales);
+ }
+ void add_version(int32_t version) {
+ fbb_.AddElement<int32_t>(Model::VT_VERSION, version, 0);
+ }
+ void add_name(flatbuffers::Offset<flatbuffers::String> name) {
+ fbb_.AddOffset(Model::VT_NAME, name);
+ }
+ void add_selection_feature_options(flatbuffers::Offset<FeatureProcessorOptions> selection_feature_options) {
+ fbb_.AddOffset(Model::VT_SELECTION_FEATURE_OPTIONS, selection_feature_options);
+ }
+ void add_classification_feature_options(flatbuffers::Offset<FeatureProcessorOptions> classification_feature_options) {
+ fbb_.AddOffset(Model::VT_CLASSIFICATION_FEATURE_OPTIONS, classification_feature_options);
+ }
+ void add_selection_model(flatbuffers::Offset<flatbuffers::Vector<uint8_t>> selection_model) {
+ fbb_.AddOffset(Model::VT_SELECTION_MODEL, selection_model);
+ }
+ void add_classification_model(flatbuffers::Offset<flatbuffers::Vector<uint8_t>> classification_model) {
+ fbb_.AddOffset(Model::VT_CLASSIFICATION_MODEL, classification_model);
+ }
+ void add_embedding_model(flatbuffers::Offset<flatbuffers::Vector<uint8_t>> embedding_model) {
+ fbb_.AddOffset(Model::VT_EMBEDDING_MODEL, embedding_model);
+ }
+ void add_selection_options(flatbuffers::Offset<SelectionModelOptions> selection_options) {
+ fbb_.AddOffset(Model::VT_SELECTION_OPTIONS, selection_options);
+ }
+ void add_classification_options(flatbuffers::Offset<ClassificationModelOptions> classification_options) {
+ fbb_.AddOffset(Model::VT_CLASSIFICATION_OPTIONS, classification_options);
+ }
+ void add_regex_model(flatbuffers::Offset<RegexModel> regex_model) {
+ fbb_.AddOffset(Model::VT_REGEX_MODEL, regex_model);
+ }
+ void add_datetime_model(flatbuffers::Offset<DatetimeModel> datetime_model) {
+ fbb_.AddOffset(Model::VT_DATETIME_MODEL, datetime_model);
+ }
+ void add_triggering_options(flatbuffers::Offset<ModelTriggeringOptions> triggering_options) {
+ fbb_.AddOffset(Model::VT_TRIGGERING_OPTIONS, triggering_options);
+ }
+ void add_enabled_modes(ModeFlag enabled_modes) {
+ fbb_.AddElement<int32_t>(Model::VT_ENABLED_MODES, static_cast<int32_t>(enabled_modes), 7);
+ }
+ void add_snap_whitespace_selections(bool snap_whitespace_selections) {
+ fbb_.AddElement<uint8_t>(Model::VT_SNAP_WHITESPACE_SELECTIONS, static_cast<uint8_t>(snap_whitespace_selections), 1);
+ }
+ void add_output_options(flatbuffers::Offset<OutputOptions> output_options) {
+ fbb_.AddOffset(Model::VT_OUTPUT_OPTIONS, output_options);
+ }
+ explicit ModelBuilder(flatbuffers::FlatBufferBuilder &_fbb)
+ : fbb_(_fbb) {
+ start_ = fbb_.StartTable();
+ }
+ ModelBuilder &operator=(const ModelBuilder &);
+ flatbuffers::Offset<Model> Finish() {
+ const auto end = fbb_.EndTable(start_);
+ auto o = flatbuffers::Offset<Model>(end);
+ return o;
+ }
+};
+
+inline flatbuffers::Offset<Model> CreateModel(
+ flatbuffers::FlatBufferBuilder &_fbb,
+ flatbuffers::Offset<flatbuffers::String> locales = 0,
+ int32_t version = 0,
+ flatbuffers::Offset<flatbuffers::String> name = 0,
+ flatbuffers::Offset<FeatureProcessorOptions> selection_feature_options = 0,
+ flatbuffers::Offset<FeatureProcessorOptions> classification_feature_options = 0,
+ flatbuffers::Offset<flatbuffers::Vector<uint8_t>> selection_model = 0,
+ flatbuffers::Offset<flatbuffers::Vector<uint8_t>> classification_model = 0,
+ flatbuffers::Offset<flatbuffers::Vector<uint8_t>> embedding_model = 0,
+ flatbuffers::Offset<SelectionModelOptions> selection_options = 0,
+ flatbuffers::Offset<ClassificationModelOptions> classification_options = 0,
+ flatbuffers::Offset<RegexModel> regex_model = 0,
+ flatbuffers::Offset<DatetimeModel> datetime_model = 0,
+ flatbuffers::Offset<ModelTriggeringOptions> triggering_options = 0,
+ ModeFlag enabled_modes = ModeFlag_ALL,
+ bool snap_whitespace_selections = true,
+ flatbuffers::Offset<OutputOptions> output_options = 0) {
+ ModelBuilder builder_(_fbb);
+ builder_.add_output_options(output_options);
+ builder_.add_enabled_modes(enabled_modes);
+ builder_.add_triggering_options(triggering_options);
+ builder_.add_datetime_model(datetime_model);
+ builder_.add_regex_model(regex_model);
+ builder_.add_classification_options(classification_options);
+ builder_.add_selection_options(selection_options);
+ builder_.add_embedding_model(embedding_model);
+ builder_.add_classification_model(classification_model);
+ builder_.add_selection_model(selection_model);
+ builder_.add_classification_feature_options(classification_feature_options);
+ builder_.add_selection_feature_options(selection_feature_options);
+ builder_.add_name(name);
+ builder_.add_version(version);
+ builder_.add_locales(locales);
+ builder_.add_snap_whitespace_selections(snap_whitespace_selections);
+ return builder_.Finish();
+}
+
+inline flatbuffers::Offset<Model> CreateModelDirect(
+ flatbuffers::FlatBufferBuilder &_fbb,
+ const char *locales = nullptr,
+ int32_t version = 0,
+ const char *name = nullptr,
+ flatbuffers::Offset<FeatureProcessorOptions> selection_feature_options = 0,
+ flatbuffers::Offset<FeatureProcessorOptions> classification_feature_options = 0,
+ const std::vector<uint8_t> *selection_model = nullptr,
+ const std::vector<uint8_t> *classification_model = nullptr,
+ const std::vector<uint8_t> *embedding_model = nullptr,
+ flatbuffers::Offset<SelectionModelOptions> selection_options = 0,
+ flatbuffers::Offset<ClassificationModelOptions> classification_options = 0,
+ flatbuffers::Offset<RegexModel> regex_model = 0,
+ flatbuffers::Offset<DatetimeModel> datetime_model = 0,
+ flatbuffers::Offset<ModelTriggeringOptions> triggering_options = 0,
+ ModeFlag enabled_modes = ModeFlag_ALL,
+ bool snap_whitespace_selections = true,
+ flatbuffers::Offset<OutputOptions> output_options = 0) {
+ return libtextclassifier2::CreateModel(
+ _fbb,
+ locales ? _fbb.CreateString(locales) : 0,
+ version,
+ name ? _fbb.CreateString(name) : 0,
+ selection_feature_options,
+ classification_feature_options,
+ selection_model ? _fbb.CreateVector<uint8_t>(*selection_model) : 0,
+ classification_model ? _fbb.CreateVector<uint8_t>(*classification_model) : 0,
+ embedding_model ? _fbb.CreateVector<uint8_t>(*embedding_model) : 0,
+ selection_options,
+ classification_options,
+ regex_model,
+ datetime_model,
+ triggering_options,
+ enabled_modes,
+ snap_whitespace_selections,
+ output_options);
+}
+
+flatbuffers::Offset<Model> CreateModel(flatbuffers::FlatBufferBuilder &_fbb, const ModelT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
+
+struct TokenizationCodepointRangeT : public flatbuffers::NativeTable {
+ typedef TokenizationCodepointRange TableType;
+ int32_t start;
+ int32_t end;
+ libtextclassifier2::TokenizationCodepointRange_::Role role;
+ int32_t script_id;
+ TokenizationCodepointRangeT()
+ : start(0),
+ end(0),
+ role(libtextclassifier2::TokenizationCodepointRange_::Role_DEFAULT_ROLE),
+ script_id(0) {
+ }
+};
+
+struct TokenizationCodepointRange FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table {
+ typedef TokenizationCodepointRangeT NativeTableType;
+ enum {
+ VT_START = 4,
+ VT_END = 6,
+ VT_ROLE = 8,
+ VT_SCRIPT_ID = 10
+ };
+ int32_t start() const {
+ return GetField<int32_t>(VT_START, 0);
+ }
+ int32_t end() const {
+ return GetField<int32_t>(VT_END, 0);
+ }
+ libtextclassifier2::TokenizationCodepointRange_::Role role() const {
+ return static_cast<libtextclassifier2::TokenizationCodepointRange_::Role>(GetField<int32_t>(VT_ROLE, 0));
+ }
+ int32_t script_id() const {
+ return GetField<int32_t>(VT_SCRIPT_ID, 0);
+ }
+ bool Verify(flatbuffers::Verifier &verifier) const {
+ return VerifyTableStart(verifier) &&
+ VerifyField<int32_t>(verifier, VT_START) &&
+ VerifyField<int32_t>(verifier, VT_END) &&
+ VerifyField<int32_t>(verifier, VT_ROLE) &&
+ VerifyField<int32_t>(verifier, VT_SCRIPT_ID) &&
+ verifier.EndTable();
+ }
+ TokenizationCodepointRangeT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ void UnPackTo(TokenizationCodepointRangeT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ static flatbuffers::Offset<TokenizationCodepointRange> Pack(flatbuffers::FlatBufferBuilder &_fbb, const TokenizationCodepointRangeT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
+};
+
+struct TokenizationCodepointRangeBuilder {
+ flatbuffers::FlatBufferBuilder &fbb_;
+ flatbuffers::uoffset_t start_;
+ void add_start(int32_t start) {
+ fbb_.AddElement<int32_t>(TokenizationCodepointRange::VT_START, start, 0);
+ }
+ void add_end(int32_t end) {
+ fbb_.AddElement<int32_t>(TokenizationCodepointRange::VT_END, end, 0);
+ }
+ void add_role(libtextclassifier2::TokenizationCodepointRange_::Role role) {
+ fbb_.AddElement<int32_t>(TokenizationCodepointRange::VT_ROLE, static_cast<int32_t>(role), 0);
+ }
+ void add_script_id(int32_t script_id) {
+ fbb_.AddElement<int32_t>(TokenizationCodepointRange::VT_SCRIPT_ID, script_id, 0);
+ }
+ explicit TokenizationCodepointRangeBuilder(flatbuffers::FlatBufferBuilder &_fbb)
+ : fbb_(_fbb) {
+ start_ = fbb_.StartTable();
+ }
+ TokenizationCodepointRangeBuilder &operator=(const TokenizationCodepointRangeBuilder &);
+ flatbuffers::Offset<TokenizationCodepointRange> Finish() {
+ const auto end = fbb_.EndTable(start_);
+ auto o = flatbuffers::Offset<TokenizationCodepointRange>(end);
+ return o;
+ }
+};
+
+inline flatbuffers::Offset<TokenizationCodepointRange> CreateTokenizationCodepointRange(
+ flatbuffers::FlatBufferBuilder &_fbb,
+ int32_t start = 0,
+ int32_t end = 0,
+ libtextclassifier2::TokenizationCodepointRange_::Role role = libtextclassifier2::TokenizationCodepointRange_::Role_DEFAULT_ROLE,
+ int32_t script_id = 0) {
+ TokenizationCodepointRangeBuilder builder_(_fbb);
+ builder_.add_script_id(script_id);
+ builder_.add_role(role);
+ builder_.add_end(end);
+ builder_.add_start(start);
+ return builder_.Finish();
+}
+
+flatbuffers::Offset<TokenizationCodepointRange> CreateTokenizationCodepointRange(flatbuffers::FlatBufferBuilder &_fbb, const TokenizationCodepointRangeT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
+
+namespace FeatureProcessorOptions_ {
+
+struct CodepointRangeT : public flatbuffers::NativeTable {
+ typedef CodepointRange TableType;
+ int32_t start;
+ int32_t end;
+ CodepointRangeT()
+ : start(0),
+ end(0) {
+ }
+};
+
+struct CodepointRange FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table {
+ typedef CodepointRangeT NativeTableType;
+ enum {
+ VT_START = 4,
+ VT_END = 6
+ };
+ int32_t start() const {
+ return GetField<int32_t>(VT_START, 0);
+ }
+ int32_t end() const {
+ return GetField<int32_t>(VT_END, 0);
+ }
+ bool Verify(flatbuffers::Verifier &verifier) const {
+ return VerifyTableStart(verifier) &&
+ VerifyField<int32_t>(verifier, VT_START) &&
+ VerifyField<int32_t>(verifier, VT_END) &&
+ verifier.EndTable();
+ }
+ CodepointRangeT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ void UnPackTo(CodepointRangeT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ static flatbuffers::Offset<CodepointRange> Pack(flatbuffers::FlatBufferBuilder &_fbb, const CodepointRangeT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
+};
+
+struct CodepointRangeBuilder {
+ flatbuffers::FlatBufferBuilder &fbb_;
+ flatbuffers::uoffset_t start_;
+ void add_start(int32_t start) {
+ fbb_.AddElement<int32_t>(CodepointRange::VT_START, start, 0);
+ }
+ void add_end(int32_t end) {
+ fbb_.AddElement<int32_t>(CodepointRange::VT_END, end, 0);
+ }
+ explicit CodepointRangeBuilder(flatbuffers::FlatBufferBuilder &_fbb)
+ : fbb_(_fbb) {
+ start_ = fbb_.StartTable();
+ }
+ CodepointRangeBuilder &operator=(const CodepointRangeBuilder &);
+ flatbuffers::Offset<CodepointRange> Finish() {
+ const auto end = fbb_.EndTable(start_);
+ auto o = flatbuffers::Offset<CodepointRange>(end);
+ return o;
+ }
+};
+
+inline flatbuffers::Offset<CodepointRange> CreateCodepointRange(
+ flatbuffers::FlatBufferBuilder &_fbb,
+ int32_t start = 0,
+ int32_t end = 0) {
+ CodepointRangeBuilder builder_(_fbb);
+ builder_.add_end(end);
+ builder_.add_start(start);
+ return builder_.Finish();
+}
+
+flatbuffers::Offset<CodepointRange> CreateCodepointRange(flatbuffers::FlatBufferBuilder &_fbb, const CodepointRangeT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
+
+struct BoundsSensitiveFeaturesT : public flatbuffers::NativeTable {
+ typedef BoundsSensitiveFeatures TableType;
+ bool enabled;
+ int32_t num_tokens_before;
+ int32_t num_tokens_inside_left;
+ int32_t num_tokens_inside_right;
+ int32_t num_tokens_after;
+ bool include_inside_bag;
+ bool include_inside_length;
+ bool score_single_token_spans_as_zero;
+ BoundsSensitiveFeaturesT()
+ : enabled(false),
+ num_tokens_before(0),
+ num_tokens_inside_left(0),
+ num_tokens_inside_right(0),
+ num_tokens_after(0),
+ include_inside_bag(false),
+ include_inside_length(false),
+ score_single_token_spans_as_zero(false) {
+ }
+};
+
+struct BoundsSensitiveFeatures FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table {
+ typedef BoundsSensitiveFeaturesT NativeTableType;
+ enum {
+ VT_ENABLED = 4,
+ VT_NUM_TOKENS_BEFORE = 6,
+ VT_NUM_TOKENS_INSIDE_LEFT = 8,
+ VT_NUM_TOKENS_INSIDE_RIGHT = 10,
+ VT_NUM_TOKENS_AFTER = 12,
+ VT_INCLUDE_INSIDE_BAG = 14,
+ VT_INCLUDE_INSIDE_LENGTH = 16,
+ VT_SCORE_SINGLE_TOKEN_SPANS_AS_ZERO = 18
+ };
+ bool enabled() const {
+ return GetField<uint8_t>(VT_ENABLED, 0) != 0;
+ }
+ int32_t num_tokens_before() const {
+ return GetField<int32_t>(VT_NUM_TOKENS_BEFORE, 0);
+ }
+ int32_t num_tokens_inside_left() const {
+ return GetField<int32_t>(VT_NUM_TOKENS_INSIDE_LEFT, 0);
+ }
+ int32_t num_tokens_inside_right() const {
+ return GetField<int32_t>(VT_NUM_TOKENS_INSIDE_RIGHT, 0);
+ }
+ int32_t num_tokens_after() const {
+ return GetField<int32_t>(VT_NUM_TOKENS_AFTER, 0);
+ }
+ bool include_inside_bag() const {
+ return GetField<uint8_t>(VT_INCLUDE_INSIDE_BAG, 0) != 0;
+ }
+ bool include_inside_length() const {
+ return GetField<uint8_t>(VT_INCLUDE_INSIDE_LENGTH, 0) != 0;
+ }
+ bool score_single_token_spans_as_zero() const {
+ return GetField<uint8_t>(VT_SCORE_SINGLE_TOKEN_SPANS_AS_ZERO, 0) != 0;
+ }
+ bool Verify(flatbuffers::Verifier &verifier) const {
+ return VerifyTableStart(verifier) &&
+ VerifyField<uint8_t>(verifier, VT_ENABLED) &&
+ VerifyField<int32_t>(verifier, VT_NUM_TOKENS_BEFORE) &&
+ VerifyField<int32_t>(verifier, VT_NUM_TOKENS_INSIDE_LEFT) &&
+ VerifyField<int32_t>(verifier, VT_NUM_TOKENS_INSIDE_RIGHT) &&
+ VerifyField<int32_t>(verifier, VT_NUM_TOKENS_AFTER) &&
+ VerifyField<uint8_t>(verifier, VT_INCLUDE_INSIDE_BAG) &&
+ VerifyField<uint8_t>(verifier, VT_INCLUDE_INSIDE_LENGTH) &&
+ VerifyField<uint8_t>(verifier, VT_SCORE_SINGLE_TOKEN_SPANS_AS_ZERO) &&
+ verifier.EndTable();
+ }
+ BoundsSensitiveFeaturesT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ void UnPackTo(BoundsSensitiveFeaturesT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ static flatbuffers::Offset<BoundsSensitiveFeatures> Pack(flatbuffers::FlatBufferBuilder &_fbb, const BoundsSensitiveFeaturesT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
+};
+
+struct BoundsSensitiveFeaturesBuilder {
+ flatbuffers::FlatBufferBuilder &fbb_;
+ flatbuffers::uoffset_t start_;
+ void add_enabled(bool enabled) {
+ fbb_.AddElement<uint8_t>(BoundsSensitiveFeatures::VT_ENABLED, static_cast<uint8_t>(enabled), 0);
+ }
+ void add_num_tokens_before(int32_t num_tokens_before) {
+ fbb_.AddElement<int32_t>(BoundsSensitiveFeatures::VT_NUM_TOKENS_BEFORE, num_tokens_before, 0);
+ }
+ void add_num_tokens_inside_left(int32_t num_tokens_inside_left) {
+ fbb_.AddElement<int32_t>(BoundsSensitiveFeatures::VT_NUM_TOKENS_INSIDE_LEFT, num_tokens_inside_left, 0);
+ }
+ void add_num_tokens_inside_right(int32_t num_tokens_inside_right) {
+ fbb_.AddElement<int32_t>(BoundsSensitiveFeatures::VT_NUM_TOKENS_INSIDE_RIGHT, num_tokens_inside_right, 0);
+ }
+ void add_num_tokens_after(int32_t num_tokens_after) {
+ fbb_.AddElement<int32_t>(BoundsSensitiveFeatures::VT_NUM_TOKENS_AFTER, num_tokens_after, 0);
+ }
+ void add_include_inside_bag(bool include_inside_bag) {
+ fbb_.AddElement<uint8_t>(BoundsSensitiveFeatures::VT_INCLUDE_INSIDE_BAG, static_cast<uint8_t>(include_inside_bag), 0);
+ }
+ void add_include_inside_length(bool include_inside_length) {
+ fbb_.AddElement<uint8_t>(BoundsSensitiveFeatures::VT_INCLUDE_INSIDE_LENGTH, static_cast<uint8_t>(include_inside_length), 0);
+ }
+ void add_score_single_token_spans_as_zero(bool score_single_token_spans_as_zero) {
+ fbb_.AddElement<uint8_t>(BoundsSensitiveFeatures::VT_SCORE_SINGLE_TOKEN_SPANS_AS_ZERO, static_cast<uint8_t>(score_single_token_spans_as_zero), 0);
+ }
+ explicit BoundsSensitiveFeaturesBuilder(flatbuffers::FlatBufferBuilder &_fbb)
+ : fbb_(_fbb) {
+ start_ = fbb_.StartTable();
+ }
+ BoundsSensitiveFeaturesBuilder &operator=(const BoundsSensitiveFeaturesBuilder &);
+ flatbuffers::Offset<BoundsSensitiveFeatures> Finish() {
+ const auto end = fbb_.EndTable(start_);
+ auto o = flatbuffers::Offset<BoundsSensitiveFeatures>(end);
+ return o;
+ }
+};
+
+inline flatbuffers::Offset<BoundsSensitiveFeatures> CreateBoundsSensitiveFeatures(
+ flatbuffers::FlatBufferBuilder &_fbb,
+ bool enabled = false,
+ int32_t num_tokens_before = 0,
+ int32_t num_tokens_inside_left = 0,
+ int32_t num_tokens_inside_right = 0,
+ int32_t num_tokens_after = 0,
+ bool include_inside_bag = false,
+ bool include_inside_length = false,
+ bool score_single_token_spans_as_zero = false) {
+ BoundsSensitiveFeaturesBuilder builder_(_fbb);
+ builder_.add_num_tokens_after(num_tokens_after);
+ builder_.add_num_tokens_inside_right(num_tokens_inside_right);
+ builder_.add_num_tokens_inside_left(num_tokens_inside_left);
+ builder_.add_num_tokens_before(num_tokens_before);
+ builder_.add_score_single_token_spans_as_zero(score_single_token_spans_as_zero);
+ builder_.add_include_inside_length(include_inside_length);
+ builder_.add_include_inside_bag(include_inside_bag);
+ builder_.add_enabled(enabled);
+ return builder_.Finish();
+}
+
+flatbuffers::Offset<BoundsSensitiveFeatures> CreateBoundsSensitiveFeatures(flatbuffers::FlatBufferBuilder &_fbb, const BoundsSensitiveFeaturesT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
+
+struct AlternativeCollectionMapEntryT : public flatbuffers::NativeTable {
+ typedef AlternativeCollectionMapEntry TableType;
+ std::string key;
+ std::string value;
+ AlternativeCollectionMapEntryT() {
+ }
+};
+
+struct AlternativeCollectionMapEntry FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table {
+ typedef AlternativeCollectionMapEntryT NativeTableType;
+ enum {
+ VT_KEY = 4,
+ VT_VALUE = 6
+ };
+ const flatbuffers::String *key() const {
+ return GetPointer<const flatbuffers::String *>(VT_KEY);
+ }
+ const flatbuffers::String *value() const {
+ return GetPointer<const flatbuffers::String *>(VT_VALUE);
+ }
+ bool Verify(flatbuffers::Verifier &verifier) const {
+ return VerifyTableStart(verifier) &&
+ VerifyOffset(verifier, VT_KEY) &&
+ verifier.Verify(key()) &&
+ VerifyOffset(verifier, VT_VALUE) &&
+ verifier.Verify(value()) &&
+ verifier.EndTable();
+ }
+ AlternativeCollectionMapEntryT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ void UnPackTo(AlternativeCollectionMapEntryT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ static flatbuffers::Offset<AlternativeCollectionMapEntry> Pack(flatbuffers::FlatBufferBuilder &_fbb, const AlternativeCollectionMapEntryT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
+};
+
+struct AlternativeCollectionMapEntryBuilder {
+ flatbuffers::FlatBufferBuilder &fbb_;
+ flatbuffers::uoffset_t start_;
+ void add_key(flatbuffers::Offset<flatbuffers::String> key) {
+ fbb_.AddOffset(AlternativeCollectionMapEntry::VT_KEY, key);
+ }
+ void add_value(flatbuffers::Offset<flatbuffers::String> value) {
+ fbb_.AddOffset(AlternativeCollectionMapEntry::VT_VALUE, value);
+ }
+ explicit AlternativeCollectionMapEntryBuilder(flatbuffers::FlatBufferBuilder &_fbb)
+ : fbb_(_fbb) {
+ start_ = fbb_.StartTable();
+ }
+ AlternativeCollectionMapEntryBuilder &operator=(const AlternativeCollectionMapEntryBuilder &);
+ flatbuffers::Offset<AlternativeCollectionMapEntry> Finish() {
+ const auto end = fbb_.EndTable(start_);
+ auto o = flatbuffers::Offset<AlternativeCollectionMapEntry>(end);
+ return o;
+ }
+};
+
+inline flatbuffers::Offset<AlternativeCollectionMapEntry> CreateAlternativeCollectionMapEntry(
+ flatbuffers::FlatBufferBuilder &_fbb,
+ flatbuffers::Offset<flatbuffers::String> key = 0,
+ flatbuffers::Offset<flatbuffers::String> value = 0) {
+ AlternativeCollectionMapEntryBuilder builder_(_fbb);
+ builder_.add_value(value);
+ builder_.add_key(key);
+ return builder_.Finish();
+}
+
+inline flatbuffers::Offset<AlternativeCollectionMapEntry> CreateAlternativeCollectionMapEntryDirect(
+ flatbuffers::FlatBufferBuilder &_fbb,
+ const char *key = nullptr,
+ const char *value = nullptr) {
+ return libtextclassifier2::FeatureProcessorOptions_::CreateAlternativeCollectionMapEntry(
+ _fbb,
+ key ? _fbb.CreateString(key) : 0,
+ value ? _fbb.CreateString(value) : 0);
+}
+
+flatbuffers::Offset<AlternativeCollectionMapEntry> CreateAlternativeCollectionMapEntry(flatbuffers::FlatBufferBuilder &_fbb, const AlternativeCollectionMapEntryT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
+
+} // namespace FeatureProcessorOptions_
+
+struct FeatureProcessorOptionsT : public flatbuffers::NativeTable {
+ typedef FeatureProcessorOptions TableType;
+ int32_t num_buckets;
+ int32_t embedding_size;
+ int32_t embedding_quantization_bits;
+ int32_t context_size;
+ int32_t max_selection_span;
+ std::vector<int32_t> chargram_orders;
+ int32_t max_word_length;
+ bool unicode_aware_features;
+ bool extract_case_feature;
+ bool extract_selection_mask_feature;
+ std::vector<std::string> regexp_feature;
+ bool remap_digits;
+ bool lowercase_tokens;
+ bool selection_reduced_output_space;
+ std::vector<std::string> collections;
+ int32_t default_collection;
+ bool only_use_line_with_click;
+ bool split_tokens_on_selection_boundaries;
+ std::vector<std::unique_ptr<TokenizationCodepointRangeT>> tokenization_codepoint_config;
+ libtextclassifier2::FeatureProcessorOptions_::CenterTokenSelectionMethod center_token_selection_method;
+ bool snap_label_span_boundaries_to_containing_tokens;
+ std::vector<std::unique_ptr<libtextclassifier2::FeatureProcessorOptions_::CodepointRangeT>> supported_codepoint_ranges;
+ std::vector<std::unique_ptr<libtextclassifier2::FeatureProcessorOptions_::CodepointRangeT>> internal_tokenizer_codepoint_ranges;
+ float min_supported_codepoint_ratio;
+ int32_t feature_version;
+ libtextclassifier2::FeatureProcessorOptions_::TokenizationType tokenization_type;
+ bool icu_preserve_whitespace_tokens;
+ std::vector<int32_t> ignored_span_boundary_codepoints;
+ std::unique_ptr<libtextclassifier2::FeatureProcessorOptions_::BoundsSensitiveFeaturesT> bounds_sensitive_features;
+ std::vector<std::string> allowed_chargrams;
+ bool tokenize_on_script_change;
+ FeatureProcessorOptionsT()
+ : num_buckets(-1),
+ embedding_size(-1),
+ embedding_quantization_bits(8),
+ context_size(-1),
+ max_selection_span(-1),
+ max_word_length(20),
+ unicode_aware_features(false),
+ extract_case_feature(false),
+ extract_selection_mask_feature(false),
+ remap_digits(false),
+ lowercase_tokens(false),
+ selection_reduced_output_space(true),
+ default_collection(-1),
+ only_use_line_with_click(false),
+ split_tokens_on_selection_boundaries(false),
+ center_token_selection_method(libtextclassifier2::FeatureProcessorOptions_::CenterTokenSelectionMethod_DEFAULT_CENTER_TOKEN_METHOD),
+ snap_label_span_boundaries_to_containing_tokens(false),
+ min_supported_codepoint_ratio(0.0f),
+ feature_version(0),
+ tokenization_type(libtextclassifier2::FeatureProcessorOptions_::TokenizationType_INTERNAL_TOKENIZER),
+ icu_preserve_whitespace_tokens(false),
+ tokenize_on_script_change(false) {
+ }
+};
+
+struct FeatureProcessorOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table {
+ typedef FeatureProcessorOptionsT NativeTableType;
+ enum {
+ VT_NUM_BUCKETS = 4,
+ VT_EMBEDDING_SIZE = 6,
+ VT_EMBEDDING_QUANTIZATION_BITS = 8,
+ VT_CONTEXT_SIZE = 10,
+ VT_MAX_SELECTION_SPAN = 12,
+ VT_CHARGRAM_ORDERS = 14,
+ VT_MAX_WORD_LENGTH = 16,
+ VT_UNICODE_AWARE_FEATURES = 18,
+ VT_EXTRACT_CASE_FEATURE = 20,
+ VT_EXTRACT_SELECTION_MASK_FEATURE = 22,
+ VT_REGEXP_FEATURE = 24,
+ VT_REMAP_DIGITS = 26,
+ VT_LOWERCASE_TOKENS = 28,
+ VT_SELECTION_REDUCED_OUTPUT_SPACE = 30,
+ VT_COLLECTIONS = 32,
+ VT_DEFAULT_COLLECTION = 34,
+ VT_ONLY_USE_LINE_WITH_CLICK = 36,
+ VT_SPLIT_TOKENS_ON_SELECTION_BOUNDARIES = 38,
+ VT_TOKENIZATION_CODEPOINT_CONFIG = 40,
+ VT_CENTER_TOKEN_SELECTION_METHOD = 42,
+ VT_SNAP_LABEL_SPAN_BOUNDARIES_TO_CONTAINING_TOKENS = 44,
+ VT_SUPPORTED_CODEPOINT_RANGES = 46,
+ VT_INTERNAL_TOKENIZER_CODEPOINT_RANGES = 48,
+ VT_MIN_SUPPORTED_CODEPOINT_RATIO = 50,
+ VT_FEATURE_VERSION = 52,
+ VT_TOKENIZATION_TYPE = 54,
+ VT_ICU_PRESERVE_WHITESPACE_TOKENS = 56,
+ VT_IGNORED_SPAN_BOUNDARY_CODEPOINTS = 58,
+ VT_BOUNDS_SENSITIVE_FEATURES = 60,
+ VT_ALLOWED_CHARGRAMS = 62,
+ VT_TOKENIZE_ON_SCRIPT_CHANGE = 64
+ };
+ int32_t num_buckets() const {
+ return GetField<int32_t>(VT_NUM_BUCKETS, -1);
+ }
+ int32_t embedding_size() const {
+ return GetField<int32_t>(VT_EMBEDDING_SIZE, -1);
+ }
+ int32_t embedding_quantization_bits() const {
+ return GetField<int32_t>(VT_EMBEDDING_QUANTIZATION_BITS, 8);
+ }
+ int32_t context_size() const {
+ return GetField<int32_t>(VT_CONTEXT_SIZE, -1);
+ }
+ int32_t max_selection_span() const {
+ return GetField<int32_t>(VT_MAX_SELECTION_SPAN, -1);
+ }
+ const flatbuffers::Vector<int32_t> *chargram_orders() const {
+ return GetPointer<const flatbuffers::Vector<int32_t> *>(VT_CHARGRAM_ORDERS);
+ }
+ int32_t max_word_length() const {
+ return GetField<int32_t>(VT_MAX_WORD_LENGTH, 20);
+ }
+ bool unicode_aware_features() const {
+ return GetField<uint8_t>(VT_UNICODE_AWARE_FEATURES, 0) != 0;
+ }
+ bool extract_case_feature() const {
+ return GetField<uint8_t>(VT_EXTRACT_CASE_FEATURE, 0) != 0;
+ }
+ bool extract_selection_mask_feature() const {
+ return GetField<uint8_t>(VT_EXTRACT_SELECTION_MASK_FEATURE, 0) != 0;
+ }
+ const flatbuffers::Vector<flatbuffers::Offset<flatbuffers::String>> *regexp_feature() const {
+ return GetPointer<const flatbuffers::Vector<flatbuffers::Offset<flatbuffers::String>> *>(VT_REGEXP_FEATURE);
+ }
+ bool remap_digits() const {
+ return GetField<uint8_t>(VT_REMAP_DIGITS, 0) != 0;
+ }
+ bool lowercase_tokens() const {
+ return GetField<uint8_t>(VT_LOWERCASE_TOKENS, 0) != 0;
+ }
+ bool selection_reduced_output_space() const {
+ return GetField<uint8_t>(VT_SELECTION_REDUCED_OUTPUT_SPACE, 1) != 0;
+ }
+ const flatbuffers::Vector<flatbuffers::Offset<flatbuffers::String>> *collections() const {
+ return GetPointer<const flatbuffers::Vector<flatbuffers::Offset<flatbuffers::String>> *>(VT_COLLECTIONS);
+ }
+ int32_t default_collection() const {
+ return GetField<int32_t>(VT_DEFAULT_COLLECTION, -1);
+ }
+ bool only_use_line_with_click() const {
+ return GetField<uint8_t>(VT_ONLY_USE_LINE_WITH_CLICK, 0) != 0;
+ }
+ bool split_tokens_on_selection_boundaries() const {
+ return GetField<uint8_t>(VT_SPLIT_TOKENS_ON_SELECTION_BOUNDARIES, 0) != 0;
+ }
+ const flatbuffers::Vector<flatbuffers::Offset<TokenizationCodepointRange>> *tokenization_codepoint_config() const {
+ return GetPointer<const flatbuffers::Vector<flatbuffers::Offset<TokenizationCodepointRange>> *>(VT_TOKENIZATION_CODEPOINT_CONFIG);
+ }
+ libtextclassifier2::FeatureProcessorOptions_::CenterTokenSelectionMethod center_token_selection_method() const {
+ return static_cast<libtextclassifier2::FeatureProcessorOptions_::CenterTokenSelectionMethod>(GetField<int32_t>(VT_CENTER_TOKEN_SELECTION_METHOD, 0));
+ }
+ bool snap_label_span_boundaries_to_containing_tokens() const {
+ return GetField<uint8_t>(VT_SNAP_LABEL_SPAN_BOUNDARIES_TO_CONTAINING_TOKENS, 0) != 0;
+ }
+ const flatbuffers::Vector<flatbuffers::Offset<libtextclassifier2::FeatureProcessorOptions_::CodepointRange>> *supported_codepoint_ranges() const {
+ return GetPointer<const flatbuffers::Vector<flatbuffers::Offset<libtextclassifier2::FeatureProcessorOptions_::CodepointRange>> *>(VT_SUPPORTED_CODEPOINT_RANGES);
+ }
+ const flatbuffers::Vector<flatbuffers::Offset<libtextclassifier2::FeatureProcessorOptions_::CodepointRange>> *internal_tokenizer_codepoint_ranges() const {
+ return GetPointer<const flatbuffers::Vector<flatbuffers::Offset<libtextclassifier2::FeatureProcessorOptions_::CodepointRange>> *>(VT_INTERNAL_TOKENIZER_CODEPOINT_RANGES);
+ }
+ float min_supported_codepoint_ratio() const {
+ return GetField<float>(VT_MIN_SUPPORTED_CODEPOINT_RATIO, 0.0f);
+ }
+ int32_t feature_version() const {
+ return GetField<int32_t>(VT_FEATURE_VERSION, 0);
+ }
+ libtextclassifier2::FeatureProcessorOptions_::TokenizationType tokenization_type() const {
+ return static_cast<libtextclassifier2::FeatureProcessorOptions_::TokenizationType>(GetField<int32_t>(VT_TOKENIZATION_TYPE, 1));
+ }
+ bool icu_preserve_whitespace_tokens() const {
+ return GetField<uint8_t>(VT_ICU_PRESERVE_WHITESPACE_TOKENS, 0) != 0;
+ }
+ const flatbuffers::Vector<int32_t> *ignored_span_boundary_codepoints() const {
+ return GetPointer<const flatbuffers::Vector<int32_t> *>(VT_IGNORED_SPAN_BOUNDARY_CODEPOINTS);
+ }
+ const libtextclassifier2::FeatureProcessorOptions_::BoundsSensitiveFeatures *bounds_sensitive_features() const {
+ return GetPointer<const libtextclassifier2::FeatureProcessorOptions_::BoundsSensitiveFeatures *>(VT_BOUNDS_SENSITIVE_FEATURES);
+ }
+ const flatbuffers::Vector<flatbuffers::Offset<flatbuffers::String>> *allowed_chargrams() const {
+ return GetPointer<const flatbuffers::Vector<flatbuffers::Offset<flatbuffers::String>> *>(VT_ALLOWED_CHARGRAMS);
+ }
+ bool tokenize_on_script_change() const {
+ return GetField<uint8_t>(VT_TOKENIZE_ON_SCRIPT_CHANGE, 0) != 0;
+ }
+ bool Verify(flatbuffers::Verifier &verifier) const {
+ return VerifyTableStart(verifier) &&
+ VerifyField<int32_t>(verifier, VT_NUM_BUCKETS) &&
+ VerifyField<int32_t>(verifier, VT_EMBEDDING_SIZE) &&
+ VerifyField<int32_t>(verifier, VT_EMBEDDING_QUANTIZATION_BITS) &&
+ VerifyField<int32_t>(verifier, VT_CONTEXT_SIZE) &&
+ VerifyField<int32_t>(verifier, VT_MAX_SELECTION_SPAN) &&
+ VerifyOffset(verifier, VT_CHARGRAM_ORDERS) &&
+ verifier.Verify(chargram_orders()) &&
+ VerifyField<int32_t>(verifier, VT_MAX_WORD_LENGTH) &&
+ VerifyField<uint8_t>(verifier, VT_UNICODE_AWARE_FEATURES) &&
+ VerifyField<uint8_t>(verifier, VT_EXTRACT_CASE_FEATURE) &&
+ VerifyField<uint8_t>(verifier, VT_EXTRACT_SELECTION_MASK_FEATURE) &&
+ VerifyOffset(verifier, VT_REGEXP_FEATURE) &&
+ verifier.Verify(regexp_feature()) &&
+ verifier.VerifyVectorOfStrings(regexp_feature()) &&
+ VerifyField<uint8_t>(verifier, VT_REMAP_DIGITS) &&
+ VerifyField<uint8_t>(verifier, VT_LOWERCASE_TOKENS) &&
+ VerifyField<uint8_t>(verifier, VT_SELECTION_REDUCED_OUTPUT_SPACE) &&
+ VerifyOffset(verifier, VT_COLLECTIONS) &&
+ verifier.Verify(collections()) &&
+ verifier.VerifyVectorOfStrings(collections()) &&
+ VerifyField<int32_t>(verifier, VT_DEFAULT_COLLECTION) &&
+ VerifyField<uint8_t>(verifier, VT_ONLY_USE_LINE_WITH_CLICK) &&
+ VerifyField<uint8_t>(verifier, VT_SPLIT_TOKENS_ON_SELECTION_BOUNDARIES) &&
+ VerifyOffset(verifier, VT_TOKENIZATION_CODEPOINT_CONFIG) &&
+ verifier.Verify(tokenization_codepoint_config()) &&
+ verifier.VerifyVectorOfTables(tokenization_codepoint_config()) &&
+ VerifyField<int32_t>(verifier, VT_CENTER_TOKEN_SELECTION_METHOD) &&
+ VerifyField<uint8_t>(verifier, VT_SNAP_LABEL_SPAN_BOUNDARIES_TO_CONTAINING_TOKENS) &&
+ VerifyOffset(verifier, VT_SUPPORTED_CODEPOINT_RANGES) &&
+ verifier.Verify(supported_codepoint_ranges()) &&
+ verifier.VerifyVectorOfTables(supported_codepoint_ranges()) &&
+ VerifyOffset(verifier, VT_INTERNAL_TOKENIZER_CODEPOINT_RANGES) &&
+ verifier.Verify(internal_tokenizer_codepoint_ranges()) &&
+ verifier.VerifyVectorOfTables(internal_tokenizer_codepoint_ranges()) &&
+ VerifyField<float>(verifier, VT_MIN_SUPPORTED_CODEPOINT_RATIO) &&
+ VerifyField<int32_t>(verifier, VT_FEATURE_VERSION) &&
+ VerifyField<int32_t>(verifier, VT_TOKENIZATION_TYPE) &&
+ VerifyField<uint8_t>(verifier, VT_ICU_PRESERVE_WHITESPACE_TOKENS) &&
+ VerifyOffset(verifier, VT_IGNORED_SPAN_BOUNDARY_CODEPOINTS) &&
+ verifier.Verify(ignored_span_boundary_codepoints()) &&
+ VerifyOffset(verifier, VT_BOUNDS_SENSITIVE_FEATURES) &&
+ verifier.VerifyTable(bounds_sensitive_features()) &&
+ VerifyOffset(verifier, VT_ALLOWED_CHARGRAMS) &&
+ verifier.Verify(allowed_chargrams()) &&
+ verifier.VerifyVectorOfStrings(allowed_chargrams()) &&
+ VerifyField<uint8_t>(verifier, VT_TOKENIZE_ON_SCRIPT_CHANGE) &&
+ verifier.EndTable();
+ }
+ FeatureProcessorOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ void UnPackTo(FeatureProcessorOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const;
+ static flatbuffers::Offset<FeatureProcessorOptions> Pack(flatbuffers::FlatBufferBuilder &_fbb, const FeatureProcessorOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
+};
+
+struct FeatureProcessorOptionsBuilder {
+ flatbuffers::FlatBufferBuilder &fbb_;
+ flatbuffers::uoffset_t start_;
+ void add_num_buckets(int32_t num_buckets) {
+ fbb_.AddElement<int32_t>(FeatureProcessorOptions::VT_NUM_BUCKETS, num_buckets, -1);
+ }
+ void add_embedding_size(int32_t embedding_size) {
+ fbb_.AddElement<int32_t>(FeatureProcessorOptions::VT_EMBEDDING_SIZE, embedding_size, -1);
+ }
+ void add_embedding_quantization_bits(int32_t embedding_quantization_bits) {
+ fbb_.AddElement<int32_t>(FeatureProcessorOptions::VT_EMBEDDING_QUANTIZATION_BITS, embedding_quantization_bits, 8);
+ }
+ void add_context_size(int32_t context_size) {
+ fbb_.AddElement<int32_t>(FeatureProcessorOptions::VT_CONTEXT_SIZE, context_size, -1);
+ }
+ void add_max_selection_span(int32_t max_selection_span) {
+ fbb_.AddElement<int32_t>(FeatureProcessorOptions::VT_MAX_SELECTION_SPAN, max_selection_span, -1);
+ }
+ void add_chargram_orders(flatbuffers::Offset<flatbuffers::Vector<int32_t>> chargram_orders) {
+ fbb_.AddOffset(FeatureProcessorOptions::VT_CHARGRAM_ORDERS, chargram_orders);
+ }
+ void add_max_word_length(int32_t max_word_length) {
+ fbb_.AddElement<int32_t>(FeatureProcessorOptions::VT_MAX_WORD_LENGTH, max_word_length, 20);
+ }
+ void add_unicode_aware_features(bool unicode_aware_features) {
+ fbb_.AddElement<uint8_t>(FeatureProcessorOptions::VT_UNICODE_AWARE_FEATURES, static_cast<uint8_t>(unicode_aware_features), 0);
+ }
+ void add_extract_case_feature(bool extract_case_feature) {
+ fbb_.AddElement<uint8_t>(FeatureProcessorOptions::VT_EXTRACT_CASE_FEATURE, static_cast<uint8_t>(extract_case_feature), 0);
+ }
+ void add_extract_selection_mask_feature(bool extract_selection_mask_feature) {
+ fbb_.AddElement<uint8_t>(FeatureProcessorOptions::VT_EXTRACT_SELECTION_MASK_FEATURE, static_cast<uint8_t>(extract_selection_mask_feature), 0);
+ }
+ void add_regexp_feature(flatbuffers::Offset<flatbuffers::Vector<flatbuffers::Offset<flatbuffers::String>>> regexp_feature) {
+ fbb_.AddOffset(FeatureProcessorOptions::VT_REGEXP_FEATURE, regexp_feature);
+ }
+ void add_remap_digits(bool remap_digits) {
+ fbb_.AddElement<uint8_t>(FeatureProcessorOptions::VT_REMAP_DIGITS, static_cast<uint8_t>(remap_digits), 0);
+ }
+ void add_lowercase_tokens(bool lowercase_tokens) {
+ fbb_.AddElement<uint8_t>(FeatureProcessorOptions::VT_LOWERCASE_TOKENS, static_cast<uint8_t>(lowercase_tokens), 0);
+ }
+ void add_selection_reduced_output_space(bool selection_reduced_output_space) {
+ fbb_.AddElement<uint8_t>(FeatureProcessorOptions::VT_SELECTION_REDUCED_OUTPUT_SPACE, static_cast<uint8_t>(selection_reduced_output_space), 1);
+ }
+ void add_collections(flatbuffers::Offset<flatbuffers::Vector<flatbuffers::Offset<flatbuffers::String>>> collections) {
+ fbb_.AddOffset(FeatureProcessorOptions::VT_COLLECTIONS, collections);
+ }
+ void add_default_collection(int32_t default_collection) {
+ fbb_.AddElement<int32_t>(FeatureProcessorOptions::VT_DEFAULT_COLLECTION, default_collection, -1);
+ }
+ void add_only_use_line_with_click(bool only_use_line_with_click) {
+ fbb_.AddElement<uint8_t>(FeatureProcessorOptions::VT_ONLY_USE_LINE_WITH_CLICK, static_cast<uint8_t>(only_use_line_with_click), 0);
+ }
+ void add_split_tokens_on_selection_boundaries(bool split_tokens_on_selection_boundaries) {
+ fbb_.AddElement<uint8_t>(FeatureProcessorOptions::VT_SPLIT_TOKENS_ON_SELECTION_BOUNDARIES, static_cast<uint8_t>(split_tokens_on_selection_boundaries), 0);
+ }
+ void add_tokenization_codepoint_config(flatbuffers::Offset<flatbuffers::Vector<flatbuffers::Offset<TokenizationCodepointRange>>> tokenization_codepoint_config) {
+ fbb_.AddOffset(FeatureProcessorOptions::VT_TOKENIZATION_CODEPOINT_CONFIG, tokenization_codepoint_config);
+ }
+ void add_center_token_selection_method(libtextclassifier2::FeatureProcessorOptions_::CenterTokenSelectionMethod center_token_selection_method) {
+ fbb_.AddElement<int32_t>(FeatureProcessorOptions::VT_CENTER_TOKEN_SELECTION_METHOD, static_cast<int32_t>(center_token_selection_method), 0);
+ }
+ void add_snap_label_span_boundaries_to_containing_tokens(bool snap_label_span_boundaries_to_containing_tokens) {
+ fbb_.AddElement<uint8_t>(FeatureProcessorOptions::VT_SNAP_LABEL_SPAN_BOUNDARIES_TO_CONTAINING_TOKENS, static_cast<uint8_t>(snap_label_span_boundaries_to_containing_tokens), 0);
+ }
+ void add_supported_codepoint_ranges(flatbuffers::Offset<flatbuffers::Vector<flatbuffers::Offset<libtextclassifier2::FeatureProcessorOptions_::CodepointRange>>> supported_codepoint_ranges) {
+ fbb_.AddOffset(FeatureProcessorOptions::VT_SUPPORTED_CODEPOINT_RANGES, supported_codepoint_ranges);
+ }
+ void add_internal_tokenizer_codepoint_ranges(flatbuffers::Offset<flatbuffers::Vector<flatbuffers::Offset<libtextclassifier2::FeatureProcessorOptions_::CodepointRange>>> internal_tokenizer_codepoint_ranges) {
+ fbb_.AddOffset(FeatureProcessorOptions::VT_INTERNAL_TOKENIZER_CODEPOINT_RANGES, internal_tokenizer_codepoint_ranges);
+ }
+ void add_min_supported_codepoint_ratio(float min_supported_codepoint_ratio) {
+ fbb_.AddElement<float>(FeatureProcessorOptions::VT_MIN_SUPPORTED_CODEPOINT_RATIO, min_supported_codepoint_ratio, 0.0f);
+ }
+ void add_feature_version(int32_t feature_version) {
+ fbb_.AddElement<int32_t>(FeatureProcessorOptions::VT_FEATURE_VERSION, feature_version, 0);
+ }
+ void add_tokenization_type(libtextclassifier2::FeatureProcessorOptions_::TokenizationType tokenization_type) {
+ fbb_.AddElement<int32_t>(FeatureProcessorOptions::VT_TOKENIZATION_TYPE, static_cast<int32_t>(tokenization_type), 1);
+ }
+ void add_icu_preserve_whitespace_tokens(bool icu_preserve_whitespace_tokens) {
+ fbb_.AddElement<uint8_t>(FeatureProcessorOptions::VT_ICU_PRESERVE_WHITESPACE_TOKENS, static_cast<uint8_t>(icu_preserve_whitespace_tokens), 0);
+ }
+ void add_ignored_span_boundary_codepoints(flatbuffers::Offset<flatbuffers::Vector<int32_t>> ignored_span_boundary_codepoints) {
+ fbb_.AddOffset(FeatureProcessorOptions::VT_IGNORED_SPAN_BOUNDARY_CODEPOINTS, ignored_span_boundary_codepoints);
+ }
+ void add_bounds_sensitive_features(flatbuffers::Offset<libtextclassifier2::FeatureProcessorOptions_::BoundsSensitiveFeatures> bounds_sensitive_features) {
+ fbb_.AddOffset(FeatureProcessorOptions::VT_BOUNDS_SENSITIVE_FEATURES, bounds_sensitive_features);
+ }
+ void add_allowed_chargrams(flatbuffers::Offset<flatbuffers::Vector<flatbuffers::Offset<flatbuffers::String>>> allowed_chargrams) {
+ fbb_.AddOffset(FeatureProcessorOptions::VT_ALLOWED_CHARGRAMS, allowed_chargrams);
+ }
+ void add_tokenize_on_script_change(bool tokenize_on_script_change) {
+ fbb_.AddElement<uint8_t>(FeatureProcessorOptions::VT_TOKENIZE_ON_SCRIPT_CHANGE, static_cast<uint8_t>(tokenize_on_script_change), 0);
+ }
+ explicit FeatureProcessorOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb)
+ : fbb_(_fbb) {
+ start_ = fbb_.StartTable();
+ }
+ FeatureProcessorOptionsBuilder &operator=(const FeatureProcessorOptionsBuilder &);
+ flatbuffers::Offset<FeatureProcessorOptions> Finish() {
+ const auto end = fbb_.EndTable(start_);
+ auto o = flatbuffers::Offset<FeatureProcessorOptions>(end);
+ return o;
+ }
+};
+
+inline flatbuffers::Offset<FeatureProcessorOptions> CreateFeatureProcessorOptions(
+ flatbuffers::FlatBufferBuilder &_fbb,
+ int32_t num_buckets = -1,
+ int32_t embedding_size = -1,
+ int32_t embedding_quantization_bits = 8,
+ int32_t context_size = -1,
+ int32_t max_selection_span = -1,
+ flatbuffers::Offset<flatbuffers::Vector<int32_t>> chargram_orders = 0,
+ int32_t max_word_length = 20,
+ bool unicode_aware_features = false,
+ bool extract_case_feature = false,
+ bool extract_selection_mask_feature = false,
+ flatbuffers::Offset<flatbuffers::Vector<flatbuffers::Offset<flatbuffers::String>>> regexp_feature = 0,
+ bool remap_digits = false,
+ bool lowercase_tokens = false,
+ bool selection_reduced_output_space = true,
+ flatbuffers::Offset<flatbuffers::Vector<flatbuffers::Offset<flatbuffers::String>>> collections = 0,
+ int32_t default_collection = -1,
+ bool only_use_line_with_click = false,
+ bool split_tokens_on_selection_boundaries = false,
+ flatbuffers::Offset<flatbuffers::Vector<flatbuffers::Offset<TokenizationCodepointRange>>> tokenization_codepoint_config = 0,
+ libtextclassifier2::FeatureProcessorOptions_::CenterTokenSelectionMethod center_token_selection_method = libtextclassifier2::FeatureProcessorOptions_::CenterTokenSelectionMethod_DEFAULT_CENTER_TOKEN_METHOD,
+ bool snap_label_span_boundaries_to_containing_tokens = false,
+ flatbuffers::Offset<flatbuffers::Vector<flatbuffers::Offset<libtextclassifier2::FeatureProcessorOptions_::CodepointRange>>> supported_codepoint_ranges = 0,
+ flatbuffers::Offset<flatbuffers::Vector<flatbuffers::Offset<libtextclassifier2::FeatureProcessorOptions_::CodepointRange>>> internal_tokenizer_codepoint_ranges = 0,
+ float min_supported_codepoint_ratio = 0.0f,
+ int32_t feature_version = 0,
+ libtextclassifier2::FeatureProcessorOptions_::TokenizationType tokenization_type = libtextclassifier2::FeatureProcessorOptions_::TokenizationType_INTERNAL_TOKENIZER,
+ bool icu_preserve_whitespace_tokens = false,
+ flatbuffers::Offset<flatbuffers::Vector<int32_t>> ignored_span_boundary_codepoints = 0,
+ flatbuffers::Offset<libtextclassifier2::FeatureProcessorOptions_::BoundsSensitiveFeatures> bounds_sensitive_features = 0,
+ flatbuffers::Offset<flatbuffers::Vector<flatbuffers::Offset<flatbuffers::String>>> allowed_chargrams = 0,
+ bool tokenize_on_script_change = false) {
+ FeatureProcessorOptionsBuilder builder_(_fbb);
+ builder_.add_allowed_chargrams(allowed_chargrams);
+ builder_.add_bounds_sensitive_features(bounds_sensitive_features);
+ builder_.add_ignored_span_boundary_codepoints(ignored_span_boundary_codepoints);
+ builder_.add_tokenization_type(tokenization_type);
+ builder_.add_feature_version(feature_version);
+ builder_.add_min_supported_codepoint_ratio(min_supported_codepoint_ratio);
+ builder_.add_internal_tokenizer_codepoint_ranges(internal_tokenizer_codepoint_ranges);
+ builder_.add_supported_codepoint_ranges(supported_codepoint_ranges);
+ builder_.add_center_token_selection_method(center_token_selection_method);
+ builder_.add_tokenization_codepoint_config(tokenization_codepoint_config);
+ builder_.add_default_collection(default_collection);
+ builder_.add_collections(collections);
+ builder_.add_regexp_feature(regexp_feature);
+ builder_.add_max_word_length(max_word_length);
+ builder_.add_chargram_orders(chargram_orders);
+ builder_.add_max_selection_span(max_selection_span);
+ builder_.add_context_size(context_size);
+ builder_.add_embedding_quantization_bits(embedding_quantization_bits);
+ builder_.add_embedding_size(embedding_size);
+ builder_.add_num_buckets(num_buckets);
+ builder_.add_tokenize_on_script_change(tokenize_on_script_change);
+ builder_.add_icu_preserve_whitespace_tokens(icu_preserve_whitespace_tokens);
+ builder_.add_snap_label_span_boundaries_to_containing_tokens(snap_label_span_boundaries_to_containing_tokens);
+ builder_.add_split_tokens_on_selection_boundaries(split_tokens_on_selection_boundaries);
+ builder_.add_only_use_line_with_click(only_use_line_with_click);
+ builder_.add_selection_reduced_output_space(selection_reduced_output_space);
+ builder_.add_lowercase_tokens(lowercase_tokens);
+ builder_.add_remap_digits(remap_digits);
+ builder_.add_extract_selection_mask_feature(extract_selection_mask_feature);
+ builder_.add_extract_case_feature(extract_case_feature);
+ builder_.add_unicode_aware_features(unicode_aware_features);
+ return builder_.Finish();
+}
+
+inline flatbuffers::Offset<FeatureProcessorOptions> CreateFeatureProcessorOptionsDirect(
+ flatbuffers::FlatBufferBuilder &_fbb,
+ int32_t num_buckets = -1,
+ int32_t embedding_size = -1,
+ int32_t embedding_quantization_bits = 8,
+ int32_t context_size = -1,
+ int32_t max_selection_span = -1,
+ const std::vector<int32_t> *chargram_orders = nullptr,
+ int32_t max_word_length = 20,
+ bool unicode_aware_features = false,
+ bool extract_case_feature = false,
+ bool extract_selection_mask_feature = false,
+ const std::vector<flatbuffers::Offset<flatbuffers::String>> *regexp_feature = nullptr,
+ bool remap_digits = false,
+ bool lowercase_tokens = false,
+ bool selection_reduced_output_space = true,
+ const std::vector<flatbuffers::Offset<flatbuffers::String>> *collections = nullptr,
+ int32_t default_collection = -1,
+ bool only_use_line_with_click = false,
+ bool split_tokens_on_selection_boundaries = false,
+ const std::vector<flatbuffers::Offset<TokenizationCodepointRange>> *tokenization_codepoint_config = nullptr,
+ libtextclassifier2::FeatureProcessorOptions_::CenterTokenSelectionMethod center_token_selection_method = libtextclassifier2::FeatureProcessorOptions_::CenterTokenSelectionMethod_DEFAULT_CENTER_TOKEN_METHOD,
+ bool snap_label_span_boundaries_to_containing_tokens = false,
+ const std::vector<flatbuffers::Offset<libtextclassifier2::FeatureProcessorOptions_::CodepointRange>> *supported_codepoint_ranges = nullptr,
+ const std::vector<flatbuffers::Offset<libtextclassifier2::FeatureProcessorOptions_::CodepointRange>> *internal_tokenizer_codepoint_ranges = nullptr,
+ float min_supported_codepoint_ratio = 0.0f,
+ int32_t feature_version = 0,
+ libtextclassifier2::FeatureProcessorOptions_::TokenizationType tokenization_type = libtextclassifier2::FeatureProcessorOptions_::TokenizationType_INTERNAL_TOKENIZER,
+ bool icu_preserve_whitespace_tokens = false,
+ const std::vector<int32_t> *ignored_span_boundary_codepoints = nullptr,
+ flatbuffers::Offset<libtextclassifier2::FeatureProcessorOptions_::BoundsSensitiveFeatures> bounds_sensitive_features = 0,
+ const std::vector<flatbuffers::Offset<flatbuffers::String>> *allowed_chargrams = nullptr,
+ bool tokenize_on_script_change = false) {
+ return libtextclassifier2::CreateFeatureProcessorOptions(
+ _fbb,
+ num_buckets,
+ embedding_size,
+ embedding_quantization_bits,
+ context_size,
+ max_selection_span,
+ chargram_orders ? _fbb.CreateVector<int32_t>(*chargram_orders) : 0,
+ max_word_length,
+ unicode_aware_features,
+ extract_case_feature,
+ extract_selection_mask_feature,
+ regexp_feature ? _fbb.CreateVector<flatbuffers::Offset<flatbuffers::String>>(*regexp_feature) : 0,
+ remap_digits,
+ lowercase_tokens,
+ selection_reduced_output_space,
+ collections ? _fbb.CreateVector<flatbuffers::Offset<flatbuffers::String>>(*collections) : 0,
+ default_collection,
+ only_use_line_with_click,
+ split_tokens_on_selection_boundaries,
+ tokenization_codepoint_config ? _fbb.CreateVector<flatbuffers::Offset<TokenizationCodepointRange>>(*tokenization_codepoint_config) : 0,
+ center_token_selection_method,
+ snap_label_span_boundaries_to_containing_tokens,
+ supported_codepoint_ranges ? _fbb.CreateVector<flatbuffers::Offset<libtextclassifier2::FeatureProcessorOptions_::CodepointRange>>(*supported_codepoint_ranges) : 0,
+ internal_tokenizer_codepoint_ranges ? _fbb.CreateVector<flatbuffers::Offset<libtextclassifier2::FeatureProcessorOptions_::CodepointRange>>(*internal_tokenizer_codepoint_ranges) : 0,
+ min_supported_codepoint_ratio,
+ feature_version,
+ tokenization_type,
+ icu_preserve_whitespace_tokens,
+ ignored_span_boundary_codepoints ? _fbb.CreateVector<int32_t>(*ignored_span_boundary_codepoints) : 0,
+ bounds_sensitive_features,
+ allowed_chargrams ? _fbb.CreateVector<flatbuffers::Offset<flatbuffers::String>>(*allowed_chargrams) : 0,
+ tokenize_on_script_change);
+}
+
+flatbuffers::Offset<FeatureProcessorOptions> CreateFeatureProcessorOptions(flatbuffers::FlatBufferBuilder &_fbb, const FeatureProcessorOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
+
+inline CompressedBufferT *CompressedBuffer::UnPack(const flatbuffers::resolver_function_t *_resolver) const {
+ auto _o = new CompressedBufferT();
+ UnPackTo(_o, _resolver);
+ return _o;
+}
+
+inline void CompressedBuffer::UnPackTo(CompressedBufferT *_o, const flatbuffers::resolver_function_t *_resolver) const {
+ (void)_o;
+ (void)_resolver;
+ { auto _e = buffer(); if (_e) { _o->buffer.resize(_e->size()); for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->buffer[_i] = _e->Get(_i); } } };
+ { auto _e = uncompressed_size(); _o->uncompressed_size = _e; };
+}
+
+inline flatbuffers::Offset<CompressedBuffer> CompressedBuffer::Pack(flatbuffers::FlatBufferBuilder &_fbb, const CompressedBufferT* _o, const flatbuffers::rehasher_function_t *_rehasher) {
+ return CreateCompressedBuffer(_fbb, _o, _rehasher);
+}
+
+inline flatbuffers::Offset<CompressedBuffer> CreateCompressedBuffer(flatbuffers::FlatBufferBuilder &_fbb, const CompressedBufferT *_o, const flatbuffers::rehasher_function_t *_rehasher) {
+ (void)_rehasher;
+ (void)_o;
+ struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const CompressedBufferT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
+ auto _buffer = _o->buffer.size() ? _fbb.CreateVector(_o->buffer) : 0;
+ auto _uncompressed_size = _o->uncompressed_size;
+ return libtextclassifier2::CreateCompressedBuffer(
+ _fbb,
+ _buffer,
+ _uncompressed_size);
+}
+
+inline SelectionModelOptionsT *SelectionModelOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const {
+ auto _o = new SelectionModelOptionsT();
+ UnPackTo(_o, _resolver);
+ return _o;
+}
+
+inline void SelectionModelOptions::UnPackTo(SelectionModelOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const {
+ (void)_o;
+ (void)_resolver;
+ { auto _e = strip_unpaired_brackets(); _o->strip_unpaired_brackets = _e; };
+ { auto _e = symmetry_context_size(); _o->symmetry_context_size = _e; };
+ { auto _e = batch_size(); _o->batch_size = _e; };
+ { auto _e = always_classify_suggested_selection(); _o->always_classify_suggested_selection = _e; };
+}
+
+inline flatbuffers::Offset<SelectionModelOptions> SelectionModelOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const SelectionModelOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) {
+ return CreateSelectionModelOptions(_fbb, _o, _rehasher);
+}
+
+inline flatbuffers::Offset<SelectionModelOptions> CreateSelectionModelOptions(flatbuffers::FlatBufferBuilder &_fbb, const SelectionModelOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) {
+ (void)_rehasher;
+ (void)_o;
+ struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const SelectionModelOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
+ auto _strip_unpaired_brackets = _o->strip_unpaired_brackets;
+ auto _symmetry_context_size = _o->symmetry_context_size;
+ auto _batch_size = _o->batch_size;
+ auto _always_classify_suggested_selection = _o->always_classify_suggested_selection;
+ return libtextclassifier2::CreateSelectionModelOptions(
+ _fbb,
+ _strip_unpaired_brackets,
+ _symmetry_context_size,
+ _batch_size,
+ _always_classify_suggested_selection);
+}
+
+inline ClassificationModelOptionsT *ClassificationModelOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const {
+ auto _o = new ClassificationModelOptionsT();
+ UnPackTo(_o, _resolver);
+ return _o;
+}
+
+inline void ClassificationModelOptions::UnPackTo(ClassificationModelOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const {
+ (void)_o;
+ (void)_resolver;
+ { auto _e = phone_min_num_digits(); _o->phone_min_num_digits = _e; };
+ { auto _e = phone_max_num_digits(); _o->phone_max_num_digits = _e; };
+ { auto _e = address_min_num_tokens(); _o->address_min_num_tokens = _e; };
+ { auto _e = max_num_tokens(); _o->max_num_tokens = _e; };
+}
+
+inline flatbuffers::Offset<ClassificationModelOptions> ClassificationModelOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const ClassificationModelOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) {
+ return CreateClassificationModelOptions(_fbb, _o, _rehasher);
+}
+
+inline flatbuffers::Offset<ClassificationModelOptions> CreateClassificationModelOptions(flatbuffers::FlatBufferBuilder &_fbb, const ClassificationModelOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) {
+ (void)_rehasher;
+ (void)_o;
+ struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const ClassificationModelOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
+ auto _phone_min_num_digits = _o->phone_min_num_digits;
+ auto _phone_max_num_digits = _o->phone_max_num_digits;
+ auto _address_min_num_tokens = _o->address_min_num_tokens;
+ auto _max_num_tokens = _o->max_num_tokens;
+ return libtextclassifier2::CreateClassificationModelOptions(
+ _fbb,
+ _phone_min_num_digits,
+ _phone_max_num_digits,
+ _address_min_num_tokens,
+ _max_num_tokens);
+}
+
+namespace RegexModel_ {
+
+inline PatternT *Pattern::UnPack(const flatbuffers::resolver_function_t *_resolver) const {
+ auto _o = new PatternT();
+ UnPackTo(_o, _resolver);
+ return _o;
+}
+
+inline void Pattern::UnPackTo(PatternT *_o, const flatbuffers::resolver_function_t *_resolver) const {
+ (void)_o;
+ (void)_resolver;
+ { auto _e = collection_name(); if (_e) _o->collection_name = _e->str(); };
+ { auto _e = pattern(); if (_e) _o->pattern = _e->str(); };
+ { auto _e = enabled_modes(); _o->enabled_modes = _e; };
+ { auto _e = target_classification_score(); _o->target_classification_score = _e; };
+ { auto _e = priority_score(); _o->priority_score = _e; };
+ { auto _e = use_approximate_matching(); _o->use_approximate_matching = _e; };
+ { auto _e = compressed_pattern(); if (_e) _o->compressed_pattern = std::unique_ptr<libtextclassifier2::CompressedBufferT>(_e->UnPack(_resolver)); };
+}
+
+inline flatbuffers::Offset<Pattern> Pattern::Pack(flatbuffers::FlatBufferBuilder &_fbb, const PatternT* _o, const flatbuffers::rehasher_function_t *_rehasher) {
+ return CreatePattern(_fbb, _o, _rehasher);
+}
+
+inline flatbuffers::Offset<Pattern> CreatePattern(flatbuffers::FlatBufferBuilder &_fbb, const PatternT *_o, const flatbuffers::rehasher_function_t *_rehasher) {
+ (void)_rehasher;
+ (void)_o;
+ struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const PatternT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
+ auto _collection_name = _o->collection_name.empty() ? 0 : _fbb.CreateString(_o->collection_name);
+ auto _pattern = _o->pattern.empty() ? 0 : _fbb.CreateString(_o->pattern);
+ auto _enabled_modes = _o->enabled_modes;
+ auto _target_classification_score = _o->target_classification_score;
+ auto _priority_score = _o->priority_score;
+ auto _use_approximate_matching = _o->use_approximate_matching;
+ auto _compressed_pattern = _o->compressed_pattern ? CreateCompressedBuffer(_fbb, _o->compressed_pattern.get(), _rehasher) : 0;
+ return libtextclassifier2::RegexModel_::CreatePattern(
+ _fbb,
+ _collection_name,
+ _pattern,
+ _enabled_modes,
+ _target_classification_score,
+ _priority_score,
+ _use_approximate_matching,
+ _compressed_pattern);
+}
+
+} // namespace RegexModel_
+
+inline RegexModelT *RegexModel::UnPack(const flatbuffers::resolver_function_t *_resolver) const {
+ auto _o = new RegexModelT();
+ UnPackTo(_o, _resolver);
+ return _o;
+}
+
+inline void RegexModel::UnPackTo(RegexModelT *_o, const flatbuffers::resolver_function_t *_resolver) const {
+ (void)_o;
+ (void)_resolver;
+ { auto _e = patterns(); if (_e) { _o->patterns.resize(_e->size()); for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->patterns[_i] = std::unique_ptr<libtextclassifier2::RegexModel_::PatternT>(_e->Get(_i)->UnPack(_resolver)); } } };
+}
+
+inline flatbuffers::Offset<RegexModel> RegexModel::Pack(flatbuffers::FlatBufferBuilder &_fbb, const RegexModelT* _o, const flatbuffers::rehasher_function_t *_rehasher) {
+ return CreateRegexModel(_fbb, _o, _rehasher);
+}
+
+inline flatbuffers::Offset<RegexModel> CreateRegexModel(flatbuffers::FlatBufferBuilder &_fbb, const RegexModelT *_o, const flatbuffers::rehasher_function_t *_rehasher) {
+ (void)_rehasher;
+ (void)_o;
+ struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const RegexModelT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
+ auto _patterns = _o->patterns.size() ? _fbb.CreateVector<flatbuffers::Offset<libtextclassifier2::RegexModel_::Pattern>> (_o->patterns.size(), [](size_t i, _VectorArgs *__va) { return CreatePattern(*__va->__fbb, __va->__o->patterns[i].get(), __va->__rehasher); }, &_va ) : 0;
+ return libtextclassifier2::CreateRegexModel(
+ _fbb,
+ _patterns);
+}
+
+namespace DatetimeModelPattern_ {
+
+inline RegexT *Regex::UnPack(const flatbuffers::resolver_function_t *_resolver) const {
+ auto _o = new RegexT();
+ UnPackTo(_o, _resolver);
+ return _o;
+}
+
+inline void Regex::UnPackTo(RegexT *_o, const flatbuffers::resolver_function_t *_resolver) const {
+ (void)_o;
+ (void)_resolver;
+ { auto _e = pattern(); if (_e) _o->pattern = _e->str(); };
+ { auto _e = groups(); if (_e) { _o->groups.resize(_e->size()); for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->groups[_i] = (DatetimeGroupType)_e->Get(_i); } } };
+ { auto _e = compressed_pattern(); if (_e) _o->compressed_pattern = std::unique_ptr<libtextclassifier2::CompressedBufferT>(_e->UnPack(_resolver)); };
+}
+
+inline flatbuffers::Offset<Regex> Regex::Pack(flatbuffers::FlatBufferBuilder &_fbb, const RegexT* _o, const flatbuffers::rehasher_function_t *_rehasher) {
+ return CreateRegex(_fbb, _o, _rehasher);
+}
+
+inline flatbuffers::Offset<Regex> CreateRegex(flatbuffers::FlatBufferBuilder &_fbb, const RegexT *_o, const flatbuffers::rehasher_function_t *_rehasher) {
+ (void)_rehasher;
+ (void)_o;
+ struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const RegexT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
+ auto _pattern = _o->pattern.empty() ? 0 : _fbb.CreateString(_o->pattern);
+ auto _groups = _o->groups.size() ? _fbb.CreateVector((const int32_t*)_o->groups.data(), _o->groups.size()) : 0;
+ auto _compressed_pattern = _o->compressed_pattern ? CreateCompressedBuffer(_fbb, _o->compressed_pattern.get(), _rehasher) : 0;
+ return libtextclassifier2::DatetimeModelPattern_::CreateRegex(
+ _fbb,
+ _pattern,
+ _groups,
+ _compressed_pattern);
+}
+
+} // namespace DatetimeModelPattern_
+
+inline DatetimeModelPatternT *DatetimeModelPattern::UnPack(const flatbuffers::resolver_function_t *_resolver) const {
+ auto _o = new DatetimeModelPatternT();
+ UnPackTo(_o, _resolver);
+ return _o;
+}
+
+inline void DatetimeModelPattern::UnPackTo(DatetimeModelPatternT *_o, const flatbuffers::resolver_function_t *_resolver) const {
+ (void)_o;
+ (void)_resolver;
+ { auto _e = regexes(); if (_e) { _o->regexes.resize(_e->size()); for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->regexes[_i] = std::unique_ptr<libtextclassifier2::DatetimeModelPattern_::RegexT>(_e->Get(_i)->UnPack(_resolver)); } } };
+ { auto _e = locales(); if (_e) { _o->locales.resize(_e->size()); for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->locales[_i] = _e->Get(_i); } } };
+ { auto _e = target_classification_score(); _o->target_classification_score = _e; };
+ { auto _e = priority_score(); _o->priority_score = _e; };
+ { auto _e = enabled_modes(); _o->enabled_modes = _e; };
+}
+
+inline flatbuffers::Offset<DatetimeModelPattern> DatetimeModelPattern::Pack(flatbuffers::FlatBufferBuilder &_fbb, const DatetimeModelPatternT* _o, const flatbuffers::rehasher_function_t *_rehasher) {
+ return CreateDatetimeModelPattern(_fbb, _o, _rehasher);
+}
+
+inline flatbuffers::Offset<DatetimeModelPattern> CreateDatetimeModelPattern(flatbuffers::FlatBufferBuilder &_fbb, const DatetimeModelPatternT *_o, const flatbuffers::rehasher_function_t *_rehasher) {
+ (void)_rehasher;
+ (void)_o;
+ struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const DatetimeModelPatternT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
+ auto _regexes = _o->regexes.size() ? _fbb.CreateVector<flatbuffers::Offset<libtextclassifier2::DatetimeModelPattern_::Regex>> (_o->regexes.size(), [](size_t i, _VectorArgs *__va) { return CreateRegex(*__va->__fbb, __va->__o->regexes[i].get(), __va->__rehasher); }, &_va ) : 0;
+ auto _locales = _o->locales.size() ? _fbb.CreateVector(_o->locales) : 0;
+ auto _target_classification_score = _o->target_classification_score;
+ auto _priority_score = _o->priority_score;
+ auto _enabled_modes = _o->enabled_modes;
+ return libtextclassifier2::CreateDatetimeModelPattern(
+ _fbb,
+ _regexes,
+ _locales,
+ _target_classification_score,
+ _priority_score,
+ _enabled_modes);
+}
+
+inline DatetimeModelExtractorT *DatetimeModelExtractor::UnPack(const flatbuffers::resolver_function_t *_resolver) const {
+ auto _o = new DatetimeModelExtractorT();
+ UnPackTo(_o, _resolver);
+ return _o;
+}
+
+inline void DatetimeModelExtractor::UnPackTo(DatetimeModelExtractorT *_o, const flatbuffers::resolver_function_t *_resolver) const {
+ (void)_o;
+ (void)_resolver;
+ { auto _e = extractor(); _o->extractor = _e; };
+ { auto _e = pattern(); if (_e) _o->pattern = _e->str(); };
+ { auto _e = locales(); if (_e) { _o->locales.resize(_e->size()); for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->locales[_i] = _e->Get(_i); } } };
+ { auto _e = compressed_pattern(); if (_e) _o->compressed_pattern = std::unique_ptr<CompressedBufferT>(_e->UnPack(_resolver)); };
+}
+
+inline flatbuffers::Offset<DatetimeModelExtractor> DatetimeModelExtractor::Pack(flatbuffers::FlatBufferBuilder &_fbb, const DatetimeModelExtractorT* _o, const flatbuffers::rehasher_function_t *_rehasher) {
+ return CreateDatetimeModelExtractor(_fbb, _o, _rehasher);
+}
+
+inline flatbuffers::Offset<DatetimeModelExtractor> CreateDatetimeModelExtractor(flatbuffers::FlatBufferBuilder &_fbb, const DatetimeModelExtractorT *_o, const flatbuffers::rehasher_function_t *_rehasher) {
+ (void)_rehasher;
+ (void)_o;
+ struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const DatetimeModelExtractorT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
+ auto _extractor = _o->extractor;
+ auto _pattern = _o->pattern.empty() ? 0 : _fbb.CreateString(_o->pattern);
+ auto _locales = _o->locales.size() ? _fbb.CreateVector(_o->locales) : 0;
+ auto _compressed_pattern = _o->compressed_pattern ? CreateCompressedBuffer(_fbb, _o->compressed_pattern.get(), _rehasher) : 0;
+ return libtextclassifier2::CreateDatetimeModelExtractor(
+ _fbb,
+ _extractor,
+ _pattern,
+ _locales,
+ _compressed_pattern);
+}
+
+inline DatetimeModelT *DatetimeModel::UnPack(const flatbuffers::resolver_function_t *_resolver) const {
+ auto _o = new DatetimeModelT();
+ UnPackTo(_o, _resolver);
+ return _o;
+}
+
+inline void DatetimeModel::UnPackTo(DatetimeModelT *_o, const flatbuffers::resolver_function_t *_resolver) const {
+ (void)_o;
+ (void)_resolver;
+ { auto _e = locales(); if (_e) { _o->locales.resize(_e->size()); for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->locales[_i] = _e->Get(_i)->str(); } } };
+ { auto _e = patterns(); if (_e) { _o->patterns.resize(_e->size()); for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->patterns[_i] = std::unique_ptr<DatetimeModelPatternT>(_e->Get(_i)->UnPack(_resolver)); } } };
+ { auto _e = extractors(); if (_e) { _o->extractors.resize(_e->size()); for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->extractors[_i] = std::unique_ptr<DatetimeModelExtractorT>(_e->Get(_i)->UnPack(_resolver)); } } };
+ { auto _e = use_extractors_for_locating(); _o->use_extractors_for_locating = _e; };
+ { auto _e = default_locales(); if (_e) { _o->default_locales.resize(_e->size()); for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->default_locales[_i] = _e->Get(_i); } } };
+}
+
+inline flatbuffers::Offset<DatetimeModel> DatetimeModel::Pack(flatbuffers::FlatBufferBuilder &_fbb, const DatetimeModelT* _o, const flatbuffers::rehasher_function_t *_rehasher) {
+ return CreateDatetimeModel(_fbb, _o, _rehasher);
+}
+
+inline flatbuffers::Offset<DatetimeModel> CreateDatetimeModel(flatbuffers::FlatBufferBuilder &_fbb, const DatetimeModelT *_o, const flatbuffers::rehasher_function_t *_rehasher) {
+ (void)_rehasher;
+ (void)_o;
+ struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const DatetimeModelT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
+ auto _locales = _o->locales.size() ? _fbb.CreateVectorOfStrings(_o->locales) : 0;
+ auto _patterns = _o->patterns.size() ? _fbb.CreateVector<flatbuffers::Offset<DatetimeModelPattern>> (_o->patterns.size(), [](size_t i, _VectorArgs *__va) { return CreateDatetimeModelPattern(*__va->__fbb, __va->__o->patterns[i].get(), __va->__rehasher); }, &_va ) : 0;
+ auto _extractors = _o->extractors.size() ? _fbb.CreateVector<flatbuffers::Offset<DatetimeModelExtractor>> (_o->extractors.size(), [](size_t i, _VectorArgs *__va) { return CreateDatetimeModelExtractor(*__va->__fbb, __va->__o->extractors[i].get(), __va->__rehasher); }, &_va ) : 0;
+ auto _use_extractors_for_locating = _o->use_extractors_for_locating;
+ auto _default_locales = _o->default_locales.size() ? _fbb.CreateVector(_o->default_locales) : 0;
+ return libtextclassifier2::CreateDatetimeModel(
+ _fbb,
+ _locales,
+ _patterns,
+ _extractors,
+ _use_extractors_for_locating,
+ _default_locales);
+}
+
+namespace DatetimeModelLibrary_ {
+
+inline ItemT *Item::UnPack(const flatbuffers::resolver_function_t *_resolver) const {
+ auto _o = new ItemT();
+ UnPackTo(_o, _resolver);
+ return _o;
+}
+
+inline void Item::UnPackTo(ItemT *_o, const flatbuffers::resolver_function_t *_resolver) const {
+ (void)_o;
+ (void)_resolver;
+ { auto _e = key(); if (_e) _o->key = _e->str(); };
+ { auto _e = value(); if (_e) _o->value = std::unique_ptr<libtextclassifier2::DatetimeModelT>(_e->UnPack(_resolver)); };
+}
+
+inline flatbuffers::Offset<Item> Item::Pack(flatbuffers::FlatBufferBuilder &_fbb, const ItemT* _o, const flatbuffers::rehasher_function_t *_rehasher) {
+ return CreateItem(_fbb, _o, _rehasher);
+}
+
+inline flatbuffers::Offset<Item> CreateItem(flatbuffers::FlatBufferBuilder &_fbb, const ItemT *_o, const flatbuffers::rehasher_function_t *_rehasher) {
+ (void)_rehasher;
+ (void)_o;
+ struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const ItemT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
+ auto _key = _o->key.empty() ? 0 : _fbb.CreateString(_o->key);
+ auto _value = _o->value ? CreateDatetimeModel(_fbb, _o->value.get(), _rehasher) : 0;
+ return libtextclassifier2::DatetimeModelLibrary_::CreateItem(
+ _fbb,
+ _key,
+ _value);
+}
+
+} // namespace DatetimeModelLibrary_
+
+inline DatetimeModelLibraryT *DatetimeModelLibrary::UnPack(const flatbuffers::resolver_function_t *_resolver) const {
+ auto _o = new DatetimeModelLibraryT();
+ UnPackTo(_o, _resolver);
+ return _o;
+}
+
+inline void DatetimeModelLibrary::UnPackTo(DatetimeModelLibraryT *_o, const flatbuffers::resolver_function_t *_resolver) const {
+ (void)_o;
+ (void)_resolver;
+ { auto _e = models(); if (_e) { _o->models.resize(_e->size()); for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->models[_i] = std::unique_ptr<libtextclassifier2::DatetimeModelLibrary_::ItemT>(_e->Get(_i)->UnPack(_resolver)); } } };
+}
+
+inline flatbuffers::Offset<DatetimeModelLibrary> DatetimeModelLibrary::Pack(flatbuffers::FlatBufferBuilder &_fbb, const DatetimeModelLibraryT* _o, const flatbuffers::rehasher_function_t *_rehasher) {
+ return CreateDatetimeModelLibrary(_fbb, _o, _rehasher);
+}
+
+inline flatbuffers::Offset<DatetimeModelLibrary> CreateDatetimeModelLibrary(flatbuffers::FlatBufferBuilder &_fbb, const DatetimeModelLibraryT *_o, const flatbuffers::rehasher_function_t *_rehasher) {
+ (void)_rehasher;
+ (void)_o;
+ struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const DatetimeModelLibraryT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
+ auto _models = _o->models.size() ? _fbb.CreateVector<flatbuffers::Offset<libtextclassifier2::DatetimeModelLibrary_::Item>> (_o->models.size(), [](size_t i, _VectorArgs *__va) { return CreateItem(*__va->__fbb, __va->__o->models[i].get(), __va->__rehasher); }, &_va ) : 0;
+ return libtextclassifier2::CreateDatetimeModelLibrary(
+ _fbb,
+ _models);
+}
+
+inline ModelTriggeringOptionsT *ModelTriggeringOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const {
+ auto _o = new ModelTriggeringOptionsT();
+ UnPackTo(_o, _resolver);
+ return _o;
+}
+
+inline void ModelTriggeringOptions::UnPackTo(ModelTriggeringOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const {
+ (void)_o;
+ (void)_resolver;
+ { auto _e = min_annotate_confidence(); _o->min_annotate_confidence = _e; };
+ { auto _e = enabled_modes(); _o->enabled_modes = _e; };
+}
+
+inline flatbuffers::Offset<ModelTriggeringOptions> ModelTriggeringOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const ModelTriggeringOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) {
+ return CreateModelTriggeringOptions(_fbb, _o, _rehasher);
+}
+
+inline flatbuffers::Offset<ModelTriggeringOptions> CreateModelTriggeringOptions(flatbuffers::FlatBufferBuilder &_fbb, const ModelTriggeringOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) {
+ (void)_rehasher;
+ (void)_o;
+ struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const ModelTriggeringOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
+ auto _min_annotate_confidence = _o->min_annotate_confidence;
+ auto _enabled_modes = _o->enabled_modes;
+ return libtextclassifier2::CreateModelTriggeringOptions(
+ _fbb,
+ _min_annotate_confidence,
+ _enabled_modes);
+}
+
+inline OutputOptionsT *OutputOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const {
+ auto _o = new OutputOptionsT();
+ UnPackTo(_o, _resolver);
+ return _o;
+}
+
+inline void OutputOptions::UnPackTo(OutputOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const {
+ (void)_o;
+ (void)_resolver;
+ { auto _e = filtered_collections_annotation(); if (_e) { _o->filtered_collections_annotation.resize(_e->size()); for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->filtered_collections_annotation[_i] = _e->Get(_i)->str(); } } };
+ { auto _e = filtered_collections_classification(); if (_e) { _o->filtered_collections_classification.resize(_e->size()); for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->filtered_collections_classification[_i] = _e->Get(_i)->str(); } } };
+ { auto _e = filtered_collections_selection(); if (_e) { _o->filtered_collections_selection.resize(_e->size()); for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->filtered_collections_selection[_i] = _e->Get(_i)->str(); } } };
+}
+
+inline flatbuffers::Offset<OutputOptions> OutputOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const OutputOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) {
+ return CreateOutputOptions(_fbb, _o, _rehasher);
+}
+
+inline flatbuffers::Offset<OutputOptions> CreateOutputOptions(flatbuffers::FlatBufferBuilder &_fbb, const OutputOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) {
+ (void)_rehasher;
+ (void)_o;
+ struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const OutputOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
+ auto _filtered_collections_annotation = _o->filtered_collections_annotation.size() ? _fbb.CreateVectorOfStrings(_o->filtered_collections_annotation) : 0;
+ auto _filtered_collections_classification = _o->filtered_collections_classification.size() ? _fbb.CreateVectorOfStrings(_o->filtered_collections_classification) : 0;
+ auto _filtered_collections_selection = _o->filtered_collections_selection.size() ? _fbb.CreateVectorOfStrings(_o->filtered_collections_selection) : 0;
+ return libtextclassifier2::CreateOutputOptions(
+ _fbb,
+ _filtered_collections_annotation,
+ _filtered_collections_classification,
+ _filtered_collections_selection);
+}
+
+inline ModelT *Model::UnPack(const flatbuffers::resolver_function_t *_resolver) const {
+ auto _o = new ModelT();
+ UnPackTo(_o, _resolver);
+ return _o;
+}
+
+inline void Model::UnPackTo(ModelT *_o, const flatbuffers::resolver_function_t *_resolver) const {
+ (void)_o;
+ (void)_resolver;
+ { auto _e = locales(); if (_e) _o->locales = _e->str(); };
+ { auto _e = version(); _o->version = _e; };
+ { auto _e = name(); if (_e) _o->name = _e->str(); };
+ { auto _e = selection_feature_options(); if (_e) _o->selection_feature_options = std::unique_ptr<FeatureProcessorOptionsT>(_e->UnPack(_resolver)); };
+ { auto _e = classification_feature_options(); if (_e) _o->classification_feature_options = std::unique_ptr<FeatureProcessorOptionsT>(_e->UnPack(_resolver)); };
+ { auto _e = selection_model(); if (_e) { _o->selection_model.resize(_e->size()); for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->selection_model[_i] = _e->Get(_i); } } };
+ { auto _e = classification_model(); if (_e) { _o->classification_model.resize(_e->size()); for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->classification_model[_i] = _e->Get(_i); } } };
+ { auto _e = embedding_model(); if (_e) { _o->embedding_model.resize(_e->size()); for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->embedding_model[_i] = _e->Get(_i); } } };
+ { auto _e = selection_options(); if (_e) _o->selection_options = std::unique_ptr<SelectionModelOptionsT>(_e->UnPack(_resolver)); };
+ { auto _e = classification_options(); if (_e) _o->classification_options = std::unique_ptr<ClassificationModelOptionsT>(_e->UnPack(_resolver)); };
+ { auto _e = regex_model(); if (_e) _o->regex_model = std::unique_ptr<RegexModelT>(_e->UnPack(_resolver)); };
+ { auto _e = datetime_model(); if (_e) _o->datetime_model = std::unique_ptr<DatetimeModelT>(_e->UnPack(_resolver)); };
+ { auto _e = triggering_options(); if (_e) _o->triggering_options = std::unique_ptr<ModelTriggeringOptionsT>(_e->UnPack(_resolver)); };
+ { auto _e = enabled_modes(); _o->enabled_modes = _e; };
+ { auto _e = snap_whitespace_selections(); _o->snap_whitespace_selections = _e; };
+ { auto _e = output_options(); if (_e) _o->output_options = std::unique_ptr<OutputOptionsT>(_e->UnPack(_resolver)); };
+}
+
+inline flatbuffers::Offset<Model> Model::Pack(flatbuffers::FlatBufferBuilder &_fbb, const ModelT* _o, const flatbuffers::rehasher_function_t *_rehasher) {
+ return CreateModel(_fbb, _o, _rehasher);
+}
+
+inline flatbuffers::Offset<Model> CreateModel(flatbuffers::FlatBufferBuilder &_fbb, const ModelT *_o, const flatbuffers::rehasher_function_t *_rehasher) {
+ (void)_rehasher;
+ (void)_o;
+ struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const ModelT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
+ auto _locales = _o->locales.empty() ? 0 : _fbb.CreateString(_o->locales);
+ auto _version = _o->version;
+ auto _name = _o->name.empty() ? 0 : _fbb.CreateString(_o->name);
+ auto _selection_feature_options = _o->selection_feature_options ? CreateFeatureProcessorOptions(_fbb, _o->selection_feature_options.get(), _rehasher) : 0;
+ auto _classification_feature_options = _o->classification_feature_options ? CreateFeatureProcessorOptions(_fbb, _o->classification_feature_options.get(), _rehasher) : 0;
+ auto _selection_model = _o->selection_model.size() ? _fbb.CreateVector(_o->selection_model) : 0;
+ auto _classification_model = _o->classification_model.size() ? _fbb.CreateVector(_o->classification_model) : 0;
+ auto _embedding_model = _o->embedding_model.size() ? _fbb.CreateVector(_o->embedding_model) : 0;
+ auto _selection_options = _o->selection_options ? CreateSelectionModelOptions(_fbb, _o->selection_options.get(), _rehasher) : 0;
+ auto _classification_options = _o->classification_options ? CreateClassificationModelOptions(_fbb, _o->classification_options.get(), _rehasher) : 0;
+ auto _regex_model = _o->regex_model ? CreateRegexModel(_fbb, _o->regex_model.get(), _rehasher) : 0;
+ auto _datetime_model = _o->datetime_model ? CreateDatetimeModel(_fbb, _o->datetime_model.get(), _rehasher) : 0;
+ auto _triggering_options = _o->triggering_options ? CreateModelTriggeringOptions(_fbb, _o->triggering_options.get(), _rehasher) : 0;
+ auto _enabled_modes = _o->enabled_modes;
+ auto _snap_whitespace_selections = _o->snap_whitespace_selections;
+ auto _output_options = _o->output_options ? CreateOutputOptions(_fbb, _o->output_options.get(), _rehasher) : 0;
+ return libtextclassifier2::CreateModel(
+ _fbb,
+ _locales,
+ _version,
+ _name,
+ _selection_feature_options,
+ _classification_feature_options,
+ _selection_model,
+ _classification_model,
+ _embedding_model,
+ _selection_options,
+ _classification_options,
+ _regex_model,
+ _datetime_model,
+ _triggering_options,
+ _enabled_modes,
+ _snap_whitespace_selections,
+ _output_options);
+}
+
+inline TokenizationCodepointRangeT *TokenizationCodepointRange::UnPack(const flatbuffers::resolver_function_t *_resolver) const {
+ auto _o = new TokenizationCodepointRangeT();
+ UnPackTo(_o, _resolver);
+ return _o;
+}
+
+inline void TokenizationCodepointRange::UnPackTo(TokenizationCodepointRangeT *_o, const flatbuffers::resolver_function_t *_resolver) const {
+ (void)_o;
+ (void)_resolver;
+ { auto _e = start(); _o->start = _e; };
+ { auto _e = end(); _o->end = _e; };
+ { auto _e = role(); _o->role = _e; };
+ { auto _e = script_id(); _o->script_id = _e; };
+}
+
+inline flatbuffers::Offset<TokenizationCodepointRange> TokenizationCodepointRange::Pack(flatbuffers::FlatBufferBuilder &_fbb, const TokenizationCodepointRangeT* _o, const flatbuffers::rehasher_function_t *_rehasher) {
+ return CreateTokenizationCodepointRange(_fbb, _o, _rehasher);
+}
+
+inline flatbuffers::Offset<TokenizationCodepointRange> CreateTokenizationCodepointRange(flatbuffers::FlatBufferBuilder &_fbb, const TokenizationCodepointRangeT *_o, const flatbuffers::rehasher_function_t *_rehasher) {
+ (void)_rehasher;
+ (void)_o;
+ struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const TokenizationCodepointRangeT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
+ auto _start = _o->start;
+ auto _end = _o->end;
+ auto _role = _o->role;
+ auto _script_id = _o->script_id;
+ return libtextclassifier2::CreateTokenizationCodepointRange(
+ _fbb,
+ _start,
+ _end,
+ _role,
+ _script_id);
+}
+
+namespace FeatureProcessorOptions_ {
+
+inline CodepointRangeT *CodepointRange::UnPack(const flatbuffers::resolver_function_t *_resolver) const {
+ auto _o = new CodepointRangeT();
+ UnPackTo(_o, _resolver);
+ return _o;
+}
+
+inline void CodepointRange::UnPackTo(CodepointRangeT *_o, const flatbuffers::resolver_function_t *_resolver) const {
+ (void)_o;
+ (void)_resolver;
+ { auto _e = start(); _o->start = _e; };
+ { auto _e = end(); _o->end = _e; };
+}
+
+inline flatbuffers::Offset<CodepointRange> CodepointRange::Pack(flatbuffers::FlatBufferBuilder &_fbb, const CodepointRangeT* _o, const flatbuffers::rehasher_function_t *_rehasher) {
+ return CreateCodepointRange(_fbb, _o, _rehasher);
+}
+
+inline flatbuffers::Offset<CodepointRange> CreateCodepointRange(flatbuffers::FlatBufferBuilder &_fbb, const CodepointRangeT *_o, const flatbuffers::rehasher_function_t *_rehasher) {
+ (void)_rehasher;
+ (void)_o;
+ struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const CodepointRangeT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
+ auto _start = _o->start;
+ auto _end = _o->end;
+ return libtextclassifier2::FeatureProcessorOptions_::CreateCodepointRange(
+ _fbb,
+ _start,
+ _end);
+}
+
+inline BoundsSensitiveFeaturesT *BoundsSensitiveFeatures::UnPack(const flatbuffers::resolver_function_t *_resolver) const {
+ auto _o = new BoundsSensitiveFeaturesT();
+ UnPackTo(_o, _resolver);
+ return _o;
+}
+
+inline void BoundsSensitiveFeatures::UnPackTo(BoundsSensitiveFeaturesT *_o, const flatbuffers::resolver_function_t *_resolver) const {
+ (void)_o;
+ (void)_resolver;
+ { auto _e = enabled(); _o->enabled = _e; };
+ { auto _e = num_tokens_before(); _o->num_tokens_before = _e; };
+ { auto _e = num_tokens_inside_left(); _o->num_tokens_inside_left = _e; };
+ { auto _e = num_tokens_inside_right(); _o->num_tokens_inside_right = _e; };
+ { auto _e = num_tokens_after(); _o->num_tokens_after = _e; };
+ { auto _e = include_inside_bag(); _o->include_inside_bag = _e; };
+ { auto _e = include_inside_length(); _o->include_inside_length = _e; };
+ { auto _e = score_single_token_spans_as_zero(); _o->score_single_token_spans_as_zero = _e; };
+}
+
+inline flatbuffers::Offset<BoundsSensitiveFeatures> BoundsSensitiveFeatures::Pack(flatbuffers::FlatBufferBuilder &_fbb, const BoundsSensitiveFeaturesT* _o, const flatbuffers::rehasher_function_t *_rehasher) {
+ return CreateBoundsSensitiveFeatures(_fbb, _o, _rehasher);
+}
+
+inline flatbuffers::Offset<BoundsSensitiveFeatures> CreateBoundsSensitiveFeatures(flatbuffers::FlatBufferBuilder &_fbb, const BoundsSensitiveFeaturesT *_o, const flatbuffers::rehasher_function_t *_rehasher) {
+ (void)_rehasher;
+ (void)_o;
+ struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const BoundsSensitiveFeaturesT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
+ auto _enabled = _o->enabled;
+ auto _num_tokens_before = _o->num_tokens_before;
+ auto _num_tokens_inside_left = _o->num_tokens_inside_left;
+ auto _num_tokens_inside_right = _o->num_tokens_inside_right;
+ auto _num_tokens_after = _o->num_tokens_after;
+ auto _include_inside_bag = _o->include_inside_bag;
+ auto _include_inside_length = _o->include_inside_length;
+ auto _score_single_token_spans_as_zero = _o->score_single_token_spans_as_zero;
+ return libtextclassifier2::FeatureProcessorOptions_::CreateBoundsSensitiveFeatures(
+ _fbb,
+ _enabled,
+ _num_tokens_before,
+ _num_tokens_inside_left,
+ _num_tokens_inside_right,
+ _num_tokens_after,
+ _include_inside_bag,
+ _include_inside_length,
+ _score_single_token_spans_as_zero);
+}
+
+inline AlternativeCollectionMapEntryT *AlternativeCollectionMapEntry::UnPack(const flatbuffers::resolver_function_t *_resolver) const {
+ auto _o = new AlternativeCollectionMapEntryT();
+ UnPackTo(_o, _resolver);
+ return _o;
+}
+
+inline void AlternativeCollectionMapEntry::UnPackTo(AlternativeCollectionMapEntryT *_o, const flatbuffers::resolver_function_t *_resolver) const {
+ (void)_o;
+ (void)_resolver;
+ { auto _e = key(); if (_e) _o->key = _e->str(); };
+ { auto _e = value(); if (_e) _o->value = _e->str(); };
+}
+
+inline flatbuffers::Offset<AlternativeCollectionMapEntry> AlternativeCollectionMapEntry::Pack(flatbuffers::FlatBufferBuilder &_fbb, const AlternativeCollectionMapEntryT* _o, const flatbuffers::rehasher_function_t *_rehasher) {
+ return CreateAlternativeCollectionMapEntry(_fbb, _o, _rehasher);
+}
+
+inline flatbuffers::Offset<AlternativeCollectionMapEntry> CreateAlternativeCollectionMapEntry(flatbuffers::FlatBufferBuilder &_fbb, const AlternativeCollectionMapEntryT *_o, const flatbuffers::rehasher_function_t *_rehasher) {
+ (void)_rehasher;
+ (void)_o;
+ struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const AlternativeCollectionMapEntryT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
+ auto _key = _o->key.empty() ? 0 : _fbb.CreateString(_o->key);
+ auto _value = _o->value.empty() ? 0 : _fbb.CreateString(_o->value);
+ return libtextclassifier2::FeatureProcessorOptions_::CreateAlternativeCollectionMapEntry(
+ _fbb,
+ _key,
+ _value);
+}
+
+} // namespace FeatureProcessorOptions_
+
+inline FeatureProcessorOptionsT *FeatureProcessorOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const {
+ auto _o = new FeatureProcessorOptionsT();
+ UnPackTo(_o, _resolver);
+ return _o;
+}
+
+inline void FeatureProcessorOptions::UnPackTo(FeatureProcessorOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const {
+ (void)_o;
+ (void)_resolver;
+ { auto _e = num_buckets(); _o->num_buckets = _e; };
+ { auto _e = embedding_size(); _o->embedding_size = _e; };
+ { auto _e = embedding_quantization_bits(); _o->embedding_quantization_bits = _e; };
+ { auto _e = context_size(); _o->context_size = _e; };
+ { auto _e = max_selection_span(); _o->max_selection_span = _e; };
+ { auto _e = chargram_orders(); if (_e) { _o->chargram_orders.resize(_e->size()); for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->chargram_orders[_i] = _e->Get(_i); } } };
+ { auto _e = max_word_length(); _o->max_word_length = _e; };
+ { auto _e = unicode_aware_features(); _o->unicode_aware_features = _e; };
+ { auto _e = extract_case_feature(); _o->extract_case_feature = _e; };
+ { auto _e = extract_selection_mask_feature(); _o->extract_selection_mask_feature = _e; };
+ { auto _e = regexp_feature(); if (_e) { _o->regexp_feature.resize(_e->size()); for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->regexp_feature[_i] = _e->Get(_i)->str(); } } };
+ { auto _e = remap_digits(); _o->remap_digits = _e; };
+ { auto _e = lowercase_tokens(); _o->lowercase_tokens = _e; };
+ { auto _e = selection_reduced_output_space(); _o->selection_reduced_output_space = _e; };
+ { auto _e = collections(); if (_e) { _o->collections.resize(_e->size()); for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->collections[_i] = _e->Get(_i)->str(); } } };
+ { auto _e = default_collection(); _o->default_collection = _e; };
+ { auto _e = only_use_line_with_click(); _o->only_use_line_with_click = _e; };
+ { auto _e = split_tokens_on_selection_boundaries(); _o->split_tokens_on_selection_boundaries = _e; };
+ { auto _e = tokenization_codepoint_config(); if (_e) { _o->tokenization_codepoint_config.resize(_e->size()); for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->tokenization_codepoint_config[_i] = std::unique_ptr<TokenizationCodepointRangeT>(_e->Get(_i)->UnPack(_resolver)); } } };
+ { auto _e = center_token_selection_method(); _o->center_token_selection_method = _e; };
+ { auto _e = snap_label_span_boundaries_to_containing_tokens(); _o->snap_label_span_boundaries_to_containing_tokens = _e; };
+ { auto _e = supported_codepoint_ranges(); if (_e) { _o->supported_codepoint_ranges.resize(_e->size()); for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->supported_codepoint_ranges[_i] = std::unique_ptr<libtextclassifier2::FeatureProcessorOptions_::CodepointRangeT>(_e->Get(_i)->UnPack(_resolver)); } } };
+ { auto _e = internal_tokenizer_codepoint_ranges(); if (_e) { _o->internal_tokenizer_codepoint_ranges.resize(_e->size()); for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->internal_tokenizer_codepoint_ranges[_i] = std::unique_ptr<libtextclassifier2::FeatureProcessorOptions_::CodepointRangeT>(_e->Get(_i)->UnPack(_resolver)); } } };
+ { auto _e = min_supported_codepoint_ratio(); _o->min_supported_codepoint_ratio = _e; };
+ { auto _e = feature_version(); _o->feature_version = _e; };
+ { auto _e = tokenization_type(); _o->tokenization_type = _e; };
+ { auto _e = icu_preserve_whitespace_tokens(); _o->icu_preserve_whitespace_tokens = _e; };
+ { auto _e = ignored_span_boundary_codepoints(); if (_e) { _o->ignored_span_boundary_codepoints.resize(_e->size()); for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->ignored_span_boundary_codepoints[_i] = _e->Get(_i); } } };
+ { auto _e = bounds_sensitive_features(); if (_e) _o->bounds_sensitive_features = std::unique_ptr<libtextclassifier2::FeatureProcessorOptions_::BoundsSensitiveFeaturesT>(_e->UnPack(_resolver)); };
+ { auto _e = allowed_chargrams(); if (_e) { _o->allowed_chargrams.resize(_e->size()); for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->allowed_chargrams[_i] = _e->Get(_i)->str(); } } };
+ { auto _e = tokenize_on_script_change(); _o->tokenize_on_script_change = _e; };
+}
+
+inline flatbuffers::Offset<FeatureProcessorOptions> FeatureProcessorOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const FeatureProcessorOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) {
+ return CreateFeatureProcessorOptions(_fbb, _o, _rehasher);
+}
+
+inline flatbuffers::Offset<FeatureProcessorOptions> CreateFeatureProcessorOptions(flatbuffers::FlatBufferBuilder &_fbb, const FeatureProcessorOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) {
+ (void)_rehasher;
+ (void)_o;
+ struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const FeatureProcessorOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
+ auto _num_buckets = _o->num_buckets;
+ auto _embedding_size = _o->embedding_size;
+ auto _embedding_quantization_bits = _o->embedding_quantization_bits;
+ auto _context_size = _o->context_size;
+ auto _max_selection_span = _o->max_selection_span;
+ auto _chargram_orders = _o->chargram_orders.size() ? _fbb.CreateVector(_o->chargram_orders) : 0;
+ auto _max_word_length = _o->max_word_length;
+ auto _unicode_aware_features = _o->unicode_aware_features;
+ auto _extract_case_feature = _o->extract_case_feature;
+ auto _extract_selection_mask_feature = _o->extract_selection_mask_feature;
+ auto _regexp_feature = _o->regexp_feature.size() ? _fbb.CreateVectorOfStrings(_o->regexp_feature) : 0;
+ auto _remap_digits = _o->remap_digits;
+ auto _lowercase_tokens = _o->lowercase_tokens;
+ auto _selection_reduced_output_space = _o->selection_reduced_output_space;
+ auto _collections = _o->collections.size() ? _fbb.CreateVectorOfStrings(_o->collections) : 0;
+ auto _default_collection = _o->default_collection;
+ auto _only_use_line_with_click = _o->only_use_line_with_click;
+ auto _split_tokens_on_selection_boundaries = _o->split_tokens_on_selection_boundaries;
+ auto _tokenization_codepoint_config = _o->tokenization_codepoint_config.size() ? _fbb.CreateVector<flatbuffers::Offset<TokenizationCodepointRange>> (_o->tokenization_codepoint_config.size(), [](size_t i, _VectorArgs *__va) { return CreateTokenizationCodepointRange(*__va->__fbb, __va->__o->tokenization_codepoint_config[i].get(), __va->__rehasher); }, &_va ) : 0;
+ auto _center_token_selection_method = _o->center_token_selection_method;
+ auto _snap_label_span_boundaries_to_containing_tokens = _o->snap_label_span_boundaries_to_containing_tokens;
+ auto _supported_codepoint_ranges = _o->supported_codepoint_ranges.size() ? _fbb.CreateVector<flatbuffers::Offset<libtextclassifier2::FeatureProcessorOptions_::CodepointRange>> (_o->supported_codepoint_ranges.size(), [](size_t i, _VectorArgs *__va) { return CreateCodepointRange(*__va->__fbb, __va->__o->supported_codepoint_ranges[i].get(), __va->__rehasher); }, &_va ) : 0;
+ auto _internal_tokenizer_codepoint_ranges = _o->internal_tokenizer_codepoint_ranges.size() ? _fbb.CreateVector<flatbuffers::Offset<libtextclassifier2::FeatureProcessorOptions_::CodepointRange>> (_o->internal_tokenizer_codepoint_ranges.size(), [](size_t i, _VectorArgs *__va) { return CreateCodepointRange(*__va->__fbb, __va->__o->internal_tokenizer_codepoint_ranges[i].get(), __va->__rehasher); }, &_va ) : 0;
+ auto _min_supported_codepoint_ratio = _o->min_supported_codepoint_ratio;
+ auto _feature_version = _o->feature_version;
+ auto _tokenization_type = _o->tokenization_type;
+ auto _icu_preserve_whitespace_tokens = _o->icu_preserve_whitespace_tokens;
+ auto _ignored_span_boundary_codepoints = _o->ignored_span_boundary_codepoints.size() ? _fbb.CreateVector(_o->ignored_span_boundary_codepoints) : 0;
+ auto _bounds_sensitive_features = _o->bounds_sensitive_features ? CreateBoundsSensitiveFeatures(_fbb, _o->bounds_sensitive_features.get(), _rehasher) : 0;
+ auto _allowed_chargrams = _o->allowed_chargrams.size() ? _fbb.CreateVectorOfStrings(_o->allowed_chargrams) : 0;
+ auto _tokenize_on_script_change = _o->tokenize_on_script_change;
+ return libtextclassifier2::CreateFeatureProcessorOptions(
+ _fbb,
+ _num_buckets,
+ _embedding_size,
+ _embedding_quantization_bits,
+ _context_size,
+ _max_selection_span,
+ _chargram_orders,
+ _max_word_length,
+ _unicode_aware_features,
+ _extract_case_feature,
+ _extract_selection_mask_feature,
+ _regexp_feature,
+ _remap_digits,
+ _lowercase_tokens,
+ _selection_reduced_output_space,
+ _collections,
+ _default_collection,
+ _only_use_line_with_click,
+ _split_tokens_on_selection_boundaries,
+ _tokenization_codepoint_config,
+ _center_token_selection_method,
+ _snap_label_span_boundaries_to_containing_tokens,
+ _supported_codepoint_ranges,
+ _internal_tokenizer_codepoint_ranges,
+ _min_supported_codepoint_ratio,
+ _feature_version,
+ _tokenization_type,
+ _icu_preserve_whitespace_tokens,
+ _ignored_span_boundary_codepoints,
+ _bounds_sensitive_features,
+ _allowed_chargrams,
+ _tokenize_on_script_change);
+}
+
+inline const libtextclassifier2::Model *GetModel(const void *buf) {
+ return flatbuffers::GetRoot<libtextclassifier2::Model>(buf);
+}
+
+inline const char *ModelIdentifier() {
+ return "TC2 ";
+}
+
+inline bool ModelBufferHasIdentifier(const void *buf) {
+ return flatbuffers::BufferHasIdentifier(
+ buf, ModelIdentifier());
+}
+
+inline bool VerifyModelBuffer(
+ flatbuffers::Verifier &verifier) {
+ return verifier.VerifyBuffer<libtextclassifier2::Model>(ModelIdentifier());
+}
+
+inline void FinishModelBuffer(
+ flatbuffers::FlatBufferBuilder &fbb,
+ flatbuffers::Offset<libtextclassifier2::Model> root) {
+ fbb.Finish(root, ModelIdentifier());
+}
+
+inline std::unique_ptr<ModelT> UnPackModel(
+ const void *buf,
+ const flatbuffers::resolver_function_t *res = nullptr) {
+ return std::unique_ptr<ModelT>(GetModel(buf)->UnPack(res));
+}
+
+} // namespace libtextclassifier2
+
+#endif // FLATBUFFERS_GENERATED_MODEL_LIBTEXTCLASSIFIER2_H_
diff --git a/models/textclassifier.ar.model b/models/textclassifier.ar.model
new file mode 100644
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diff --git a/models/textclassifier.langid.model b/models/textclassifier.langid.model
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diff --git a/models/textclassifier.smartselection.zh-Hant.model b/models/textclassifier.smartselection.zh-Hant.model
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diff --git a/models/textclassifier.universal.model b/models/textclassifier.universal.model
new file mode 100644
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diff --git a/models/textclassifier.zh-Hant.model b/models/textclassifier.zh-Hant.model
new file mode 100644
index 0000000..32edfe4
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diff --git a/models/textclassifier.zh.model b/models/textclassifier.zh.model
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diff --git a/models/update.sh b/models/update.sh
new file mode 100755
index 0000000..8b60d2f
--- /dev/null
+++ b/models/update.sh
@@ -0,0 +1,12 @@
+#!/bin/bash
+# Updates the set of model with the most recent ones.
+
+set -e
+
+BASE_URL=https://www.gstatic.com/android/text_classifier/p/live
+
+cd "$(dirname "$0")"
+
+for f in $(wget -O- "$BASE_URL/FILELIST"); do
+ wget "$BASE_URL/$f" -O "$f"
+done
diff --git a/quantization.cc b/quantization.cc
new file mode 100644
index 0000000..1a34565
--- /dev/null
+++ b/quantization.cc
@@ -0,0 +1,92 @@
+/*
+ * Copyright (C) 2017 The Android Open Source Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "quantization.h"
+
+#include "util/base/logging.h"
+
+namespace libtextclassifier2 {
+namespace {
+float DequantizeValue(int num_sparse_features, int quantization_bias,
+ float multiplier, int value) {
+ return 1.0 / num_sparse_features * (value - quantization_bias) * multiplier;
+}
+
+void DequantizeAdd8bit(const float* scales, const uint8* embeddings,
+ int bytes_per_embedding, const int num_sparse_features,
+ const int bucket_id, float* dest, int dest_size) {
+ static const int kQuantizationBias8bit = 128;
+ const float multiplier = scales[bucket_id];
+ for (int k = 0; k < dest_size; ++k) {
+ dest[k] +=
+ DequantizeValue(num_sparse_features, kQuantizationBias8bit, multiplier,
+ embeddings[bucket_id * bytes_per_embedding + k]);
+ }
+}
+
+void DequantizeAddNBit(const float* scales, const uint8* embeddings,
+ int bytes_per_embedding, int num_sparse_features,
+ int quantization_bits, int bucket_id, float* dest,
+ int dest_size) {
+ const int quantization_bias = 1 << (quantization_bits - 1);
+ const float multiplier = scales[bucket_id];
+ for (int i = 0; i < dest_size; ++i) {
+ const int bit_offset = i * quantization_bits;
+ const int read16_offset = bit_offset / 8;
+
+ uint16 data = embeddings[bucket_id * bytes_per_embedding + read16_offset];
+ // If we are not at the end of the embedding row, we can read 2-byte uint16,
+ // but if we are, we need to only read uint8.
+ if (read16_offset < bytes_per_embedding - 1) {
+ data |= embeddings[bucket_id * bytes_per_embedding + read16_offset + 1]
+ << 8;
+ }
+ int value = (data >> (bit_offset % 8)) & ((1 << quantization_bits) - 1);
+ dest[i] += DequantizeValue(num_sparse_features, quantization_bias,
+ multiplier, value);
+ }
+}
+} // namespace
+
+bool CheckQuantizationParams(int bytes_per_embedding, int quantization_bits,
+ int output_embedding_size) {
+ if (bytes_per_embedding * 8 / quantization_bits < output_embedding_size) {
+ return false;
+ }
+
+ return true;
+}
+
+bool DequantizeAdd(const float* scales, const uint8* embeddings,
+ int bytes_per_embedding, int num_sparse_features,
+ int quantization_bits, int bucket_id, float* dest,
+ int dest_size) {
+ if (quantization_bits == 8) {
+ DequantizeAdd8bit(scales, embeddings, bytes_per_embedding,
+ num_sparse_features, bucket_id, dest, dest_size);
+ } else if (quantization_bits != 8) {
+ DequantizeAddNBit(scales, embeddings, bytes_per_embedding,
+ num_sparse_features, quantization_bits, bucket_id, dest,
+ dest_size);
+ } else {
+ TC_LOG(ERROR) << "Unsupported quantization_bits: " << quantization_bits;
+ return false;
+ }
+
+ return true;
+}
+
+} // namespace libtextclassifier2
diff --git a/quantization.h b/quantization.h
new file mode 100644
index 0000000..c486640
--- /dev/null
+++ b/quantization.h
@@ -0,0 +1,39 @@
+/*
+ * Copyright (C) 2017 The Android Open Source Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#ifndef LIBTEXTCLASSIFIER_QUANTIZATION_H_
+#define LIBTEXTCLASSIFIER_QUANTIZATION_H_
+
+#include "util/base/integral_types.h"
+
+namespace libtextclassifier2 {
+
+// Returns true if the quantization parameters are valid.
+bool CheckQuantizationParams(int bytes_per_embedding, int quantization_bits,
+ int output_embedding_size);
+
+// Dequantizes embeddings (quantized to 1 to 8 bits) into the floats they
+// represent. The algorithm proceeds by reading 2-byte words from the embedding
+// storage to handle well the cases when the quantized value crosses the byte-
+// boundary.
+bool DequantizeAdd(const float* scales, const uint8* embeddings,
+ int bytes_per_embedding, int num_sparse_features,
+ int quantization_bits, int bucket_id, float* dest,
+ int dest_size);
+
+} // namespace libtextclassifier2
+
+#endif // LIBTEXTCLASSIFIER_QUANTIZATION_H_
diff --git a/quantization_test.cc b/quantization_test.cc
new file mode 100644
index 0000000..088daaf
--- /dev/null
+++ b/quantization_test.cc
@@ -0,0 +1,163 @@
+/*
+ * Copyright (C) 2017 The Android Open Source Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "quantization.h"
+
+#include <vector>
+
+#include "gmock/gmock.h"
+#include "gtest/gtest.h"
+
+using testing::ElementsAreArray;
+using testing::FloatEq;
+using testing::Matcher;
+
+namespace libtextclassifier2 {
+namespace {
+
+Matcher<std::vector<float>> ElementsAreFloat(const std::vector<float>& values) {
+ std::vector<Matcher<float>> matchers;
+ for (const float value : values) {
+ matchers.push_back(FloatEq(value));
+ }
+ return ElementsAreArray(matchers);
+}
+
+TEST(QuantizationTest, DequantizeAdd8bit) {
+ std::vector<float> scales{{0.1, 9.0, -7.0}};
+ std::vector<uint8> embeddings{{/*0: */ 0x00, 0xFF, 0x09, 0x00,
+ /*1: */ 0xFF, 0x09, 0x00, 0xFF,
+ /*2: */ 0x09, 0x00, 0xFF, 0x09}};
+
+ const int quantization_bits = 8;
+ const int bytes_per_embedding = 4;
+ const int num_sparse_features = 7;
+ {
+ const int bucket_id = 0;
+ std::vector<float> dest(4, 0.0);
+ DequantizeAdd(scales.data(), embeddings.data(), bytes_per_embedding,
+ num_sparse_features, quantization_bits, bucket_id,
+ dest.data(), dest.size());
+
+ EXPECT_THAT(dest,
+ ElementsAreFloat(std::vector<float>{
+ // clang-format off
+ {1.0 / 7 * 0.1 * (0x00 - 128),
+ 1.0 / 7 * 0.1 * (0xFF - 128),
+ 1.0 / 7 * 0.1 * (0x09 - 128),
+ 1.0 / 7 * 0.1 * (0x00 - 128)}
+ // clang-format on
+ }));
+ }
+
+ {
+ const int bucket_id = 1;
+ std::vector<float> dest(4, 0.0);
+ DequantizeAdd(scales.data(), embeddings.data(), bytes_per_embedding,
+ num_sparse_features, quantization_bits, bucket_id,
+ dest.data(), dest.size());
+
+ EXPECT_THAT(dest,
+ ElementsAreFloat(std::vector<float>{
+ // clang-format off
+ {1.0 / 7 * 9.0 * (0xFF - 128),
+ 1.0 / 7 * 9.0 * (0x09 - 128),
+ 1.0 / 7 * 9.0 * (0x00 - 128),
+ 1.0 / 7 * 9.0 * (0xFF - 128)}
+ // clang-format on
+ }));
+ }
+}
+
+TEST(QuantizationTest, DequantizeAdd1bitZeros) {
+ const int bytes_per_embedding = 4;
+ const int num_buckets = 3;
+ const int num_sparse_features = 7;
+ const int quantization_bits = 1;
+ const int bucket_id = 1;
+
+ std::vector<float> scales(num_buckets);
+ std::vector<uint8> embeddings(bytes_per_embedding * num_buckets);
+ std::fill(scales.begin(), scales.end(), 1);
+ std::fill(embeddings.begin(), embeddings.end(), 0);
+
+ std::vector<float> dest(32);
+ DequantizeAdd(scales.data(), embeddings.data(), bytes_per_embedding,
+ num_sparse_features, quantization_bits, bucket_id, dest.data(),
+ dest.size());
+
+ std::vector<float> expected(32);
+ std::fill(expected.begin(), expected.end(),
+ 1.0 / num_sparse_features * (0 - 1));
+ EXPECT_THAT(dest, ElementsAreFloat(expected));
+}
+
+TEST(QuantizationTest, DequantizeAdd1bitOnes) {
+ const int bytes_per_embedding = 4;
+ const int num_buckets = 3;
+ const int num_sparse_features = 7;
+ const int quantization_bits = 1;
+ const int bucket_id = 1;
+
+ std::vector<float> scales(num_buckets, 1.0);
+ std::vector<uint8> embeddings(bytes_per_embedding * num_buckets, 0xFF);
+
+ std::vector<float> dest(32);
+ DequantizeAdd(scales.data(), embeddings.data(), bytes_per_embedding,
+ num_sparse_features, quantization_bits, bucket_id, dest.data(),
+ dest.size());
+ std::vector<float> expected(32);
+ std::fill(expected.begin(), expected.end(),
+ 1.0 / num_sparse_features * (1 - 1));
+ EXPECT_THAT(dest, ElementsAreFloat(expected));
+}
+
+TEST(QuantizationTest, DequantizeAdd3bit) {
+ const int bytes_per_embedding = 4;
+ const int num_buckets = 3;
+ const int num_sparse_features = 7;
+ const int quantization_bits = 3;
+ const int bucket_id = 1;
+
+ std::vector<float> scales(num_buckets, 1.0);
+ scales[1] = 9.0;
+ std::vector<uint8> embeddings(bytes_per_embedding * num_buckets, 0);
+ // For bucket_id=1, the embedding has values 0..9 for indices 0..9:
+ embeddings[4] = (1 << 7) | (1 << 6) | (1 << 4) | 1;
+ embeddings[5] = (1 << 6) | (1 << 4) | (1 << 3);
+ embeddings[6] = (1 << 4) | (1 << 3) | (1 << 2) | (1 << 1) | 1;
+
+ std::vector<float> dest(10);
+ DequantizeAdd(scales.data(), embeddings.data(), bytes_per_embedding,
+ num_sparse_features, quantization_bits, bucket_id, dest.data(),
+ dest.size());
+
+ std::vector<float> expected;
+ expected.push_back(1.0 / num_sparse_features * (1 - 4) * scales[bucket_id]);
+ expected.push_back(1.0 / num_sparse_features * (2 - 4) * scales[bucket_id]);
+ expected.push_back(1.0 / num_sparse_features * (3 - 4) * scales[bucket_id]);
+ expected.push_back(1.0 / num_sparse_features * (4 - 4) * scales[bucket_id]);
+ expected.push_back(1.0 / num_sparse_features * (5 - 4) * scales[bucket_id]);
+ expected.push_back(1.0 / num_sparse_features * (6 - 4) * scales[bucket_id]);
+ expected.push_back(1.0 / num_sparse_features * (7 - 4) * scales[bucket_id]);
+ expected.push_back(1.0 / num_sparse_features * (0 - 4) * scales[bucket_id]);
+ expected.push_back(1.0 / num_sparse_features * (0 - 4) * scales[bucket_id]);
+ expected.push_back(1.0 / num_sparse_features * (0 - 4) * scales[bucket_id]);
+ EXPECT_THAT(dest, ElementsAreFloat(expected));
+}
+
+} // namespace
+} // namespace libtextclassifier2
diff --git a/smartselect/cached-features.cc b/smartselect/cached-features.cc
deleted file mode 100644
index c249db9..0000000
--- a/smartselect/cached-features.cc
+++ /dev/null
@@ -1,82 +0,0 @@
-/*
- * Copyright (C) 2017 The Android Open Source Project
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#include "smartselect/cached-features.h"
-#include "util/base/logging.h"
-
-namespace libtextclassifier {
-
-void CachedFeatures::Extract(
- const std::vector<std::vector<int>>& sparse_features,
- const std::vector<std::vector<float>>& dense_features,
- const std::function<bool(const std::vector<int>&, const std::vector<float>&,
- float*)>& feature_vector_fn) {
- features_.resize(feature_vector_size_ * tokens_.size());
- for (int i = 0; i < tokens_.size(); ++i) {
- feature_vector_fn(sparse_features[i], dense_features[i],
- features_.data() + i * feature_vector_size_);
- }
-}
-
-bool CachedFeatures::Get(int click_pos, VectorSpan<float>* features,
- VectorSpan<Token>* output_tokens) {
- const int token_start = click_pos - context_size_;
- const int token_end = click_pos + context_size_ + 1;
- if (token_start < 0 || token_end > tokens_.size()) {
- TC_LOG(ERROR) << "Tokens out of range: " << token_start << " " << token_end;
- return false;
- }
-
- *features =
- VectorSpan<float>(features_.begin() + token_start * feature_vector_size_,
- features_.begin() + token_end * feature_vector_size_);
- *output_tokens = VectorSpan<Token>(tokens_.begin() + token_start,
- tokens_.begin() + token_end);
- if (remap_v0_feature_vector_) {
- RemapV0FeatureVector(features);
- }
-
- return true;
-}
-
-void CachedFeatures::RemapV0FeatureVector(VectorSpan<float>* features) {
- if (!remap_v0_feature_vector_) {
- return;
- }
-
- auto it = features->begin();
- int num_suffix_features =
- feature_vector_size_ - remap_v0_chargram_embedding_size_;
- int num_tokens = context_size_ * 2 + 1;
- for (int t = 0; t < num_tokens; ++t) {
- for (int i = 0; i < remap_v0_chargram_embedding_size_; ++i) {
- v0_feature_storage_[t * remap_v0_chargram_embedding_size_ + i] = *it;
- ++it;
- }
- // Rest of the features are the dense features that come to the end.
- for (int i = 0; i < num_suffix_features; ++i) {
- // clang-format off
- v0_feature_storage_[num_tokens * remap_v0_chargram_embedding_size_
- + t * num_suffix_features
- + i] = *it;
- // clang-format on
- ++it;
- }
- }
- *features = VectorSpan<float>(v0_feature_storage_);
-}
-
-} // namespace libtextclassifier
diff --git a/smartselect/cached-features.h b/smartselect/cached-features.h
deleted file mode 100644
index 990233c..0000000
--- a/smartselect/cached-features.h
+++ /dev/null
@@ -1,105 +0,0 @@
-/*
- * Copyright (C) 2017 The Android Open Source Project
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#ifndef LIBTEXTCLASSIFIER_SMARTSELECT_CACHED_FEATURES_H_
-#define LIBTEXTCLASSIFIER_SMARTSELECT_CACHED_FEATURES_H_
-
-#include <memory>
-#include <vector>
-
-#include "common/vector-span.h"
-#include "smartselect/types.h"
-
-namespace libtextclassifier {
-
-// Holds state for extracting features across multiple calls and reusing them.
-// Assumes that features for each Token are independent.
-class CachedFeatures {
- public:
- // Extracts the features for the given sequence of tokens.
- // - context_size: Specifies how many tokens to the left, and how many
- // tokens to the right spans the context.
- // - sparse_features, dense_features: Extracted features for each token.
- // - feature_vector_fn: Writes features for given Token to the specified
- // storage.
- // NOTE: The function can assume that the underlying
- // storage is initialized to all zeros.
- // - feature_vector_size: Size of a feature vector for one Token.
- CachedFeatures(VectorSpan<Token> tokens, int context_size,
- const std::vector<std::vector<int>>& sparse_features,
- const std::vector<std::vector<float>>& dense_features,
- const std::function<bool(const std::vector<int>&,
- const std::vector<float>&, float*)>&
- feature_vector_fn,
- int feature_vector_size)
- : tokens_(tokens),
- context_size_(context_size),
- feature_vector_size_(feature_vector_size),
- remap_v0_feature_vector_(false),
- remap_v0_chargram_embedding_size_(-1) {
- Extract(sparse_features, dense_features, feature_vector_fn);
- }
-
- // Gets a VectorSpan with the features for given click position.
- bool Get(int click_pos, VectorSpan<float>* features,
- VectorSpan<Token>* output_tokens);
-
- // Turns on a compatibility mode, which re-maps the extracted features to the
- // v0 feature format (where the dense features were at the end).
- // WARNING: Internally v0_feature_storage_ is used as a backing buffer for
- // VectorSpan<float>, so the output of Extract is valid only until the next
- // call or destruction of the current CachedFeatures object.
- // TODO(zilka): Remove when we'll have retrained models.
- void SetV0FeatureMode(int chargram_embedding_size) {
- remap_v0_feature_vector_ = true;
- remap_v0_chargram_embedding_size_ = chargram_embedding_size;
- v0_feature_storage_.resize(feature_vector_size_ * (context_size_ * 2 + 1));
- }
-
- protected:
- // Extracts features for all tokens and stores them for later retrieval.
- void Extract(const std::vector<std::vector<int>>& sparse_features,
- const std::vector<std::vector<float>>& dense_features,
- const std::function<bool(const std::vector<int>&,
- const std::vector<float>&, float*)>&
- feature_vector_fn);
-
- // Remaps extracted features to V0 feature format. The mapping is using
- // the v0_feature_storage_ as the backing storage for the mapped features.
- // For each token the features consist of:
- // - chargram embeddings
- // - dense features
- // They are concatenated together as [chargram embeddings; dense features]
- // for each token independently.
- // The V0 features require that the chargram embeddings for tokens are
- // concatenated first together, and at the end, the dense features for the
- // tokens are concatenated to it.
- void RemapV0FeatureVector(VectorSpan<float>* features);
-
- private:
- const VectorSpan<Token> tokens_;
- const int context_size_;
- const int feature_vector_size_;
- bool remap_v0_feature_vector_;
- int remap_v0_chargram_embedding_size_;
-
- std::vector<float> features_;
- std::vector<float> v0_feature_storage_;
-};
-
-} // namespace libtextclassifier
-
-#endif // LIBTEXTCLASSIFIER_SMARTSELECT_CACHED_FEATURES_H_
diff --git a/smartselect/cached-features_test.cc b/smartselect/cached-features_test.cc
deleted file mode 100644
index b456816..0000000
--- a/smartselect/cached-features_test.cc
+++ /dev/null
@@ -1,149 +0,0 @@
-/*
- * Copyright (C) 2017 The Android Open Source Project
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#include "smartselect/cached-features.h"
-
-#include "gmock/gmock.h"
-#include "gtest/gtest.h"
-
-namespace libtextclassifier {
-namespace {
-
-class TestingCachedFeatures : public CachedFeatures {
- public:
- using CachedFeatures::CachedFeatures;
- using CachedFeatures::RemapV0FeatureVector;
-};
-
-TEST(CachedFeaturesTest, Simple) {
- std::vector<Token> tokens;
- tokens.push_back(Token());
- tokens.push_back(Token());
- tokens.push_back(Token("Hello", 0, 1));
- tokens.push_back(Token("World", 1, 2));
- tokens.push_back(Token("today!", 2, 3));
- tokens.push_back(Token());
- tokens.push_back(Token());
-
- std::vector<std::vector<int>> sparse_features(tokens.size());
- for (int i = 0; i < sparse_features.size(); ++i) {
- sparse_features[i].push_back(i);
- }
- std::vector<std::vector<float>> dense_features(tokens.size());
- for (int i = 0; i < dense_features.size(); ++i) {
- dense_features[i].push_back(-i);
- }
-
- TestingCachedFeatures feature_extractor(
- tokens, /*context_size=*/2, sparse_features, dense_features,
- [](const std::vector<int>& sparse_features,
- const std::vector<float>& dense_features, float* features) {
- features[0] = sparse_features[0];
- features[1] = sparse_features[0];
- features[2] = dense_features[0];
- features[3] = dense_features[0];
- features[4] = 123;
- return true;
- },
- 5);
-
- VectorSpan<float> features;
- VectorSpan<Token> output_tokens;
- EXPECT_TRUE(feature_extractor.Get(2, &features, &output_tokens));
- for (int i = 0; i < 5; i++) {
- EXPECT_EQ(features[i * 5 + 0], i) << "Feature " << i;
- EXPECT_EQ(features[i * 5 + 1], i) << "Feature " << i;
- EXPECT_EQ(features[i * 5 + 2], -i) << "Feature " << i;
- EXPECT_EQ(features[i * 5 + 3], -i) << "Feature " << i;
- EXPECT_EQ(features[i * 5 + 4], 123) << "Feature " << i;
- }
-}
-
-TEST(CachedFeaturesTest, InvalidInput) {
- std::vector<Token> tokens;
- tokens.push_back(Token());
- tokens.push_back(Token());
- tokens.push_back(Token("Hello", 0, 1));
- tokens.push_back(Token("World", 1, 2));
- tokens.push_back(Token("today!", 2, 3));
- tokens.push_back(Token());
- tokens.push_back(Token());
-
- std::vector<std::vector<int>> sparse_features(tokens.size());
- std::vector<std::vector<float>> dense_features(tokens.size());
-
- TestingCachedFeatures feature_extractor(
- tokens, /*context_size=*/2, sparse_features, dense_features,
- [](const std::vector<int>& sparse_features,
- const std::vector<float>& dense_features,
- float* features) { return true; },
- /*feature_vector_size=*/5);
-
- VectorSpan<float> features;
- VectorSpan<Token> output_tokens;
- EXPECT_FALSE(feature_extractor.Get(-1000, &features, &output_tokens));
- EXPECT_FALSE(feature_extractor.Get(-1, &features, &output_tokens));
- EXPECT_FALSE(feature_extractor.Get(0, &features, &output_tokens));
- EXPECT_TRUE(feature_extractor.Get(2, &features, &output_tokens));
- EXPECT_TRUE(feature_extractor.Get(4, &features, &output_tokens));
- EXPECT_FALSE(feature_extractor.Get(5, &features, &output_tokens));
- EXPECT_FALSE(feature_extractor.Get(500, &features, &output_tokens));
-}
-
-TEST(CachedFeaturesTest, RemapV0FeatureVector) {
- std::vector<Token> tokens;
- tokens.push_back(Token());
- tokens.push_back(Token());
- tokens.push_back(Token("Hello", 0, 1));
- tokens.push_back(Token("World", 1, 2));
- tokens.push_back(Token("today!", 2, 3));
- tokens.push_back(Token());
- tokens.push_back(Token());
-
- std::vector<std::vector<int>> sparse_features(tokens.size());
- std::vector<std::vector<float>> dense_features(tokens.size());
-
- TestingCachedFeatures feature_extractor(
- tokens, /*context_size=*/2, sparse_features, dense_features,
- [](const std::vector<int>& sparse_features,
- const std::vector<float>& dense_features,
- float* features) { return true; },
- /*feature_vector_size=*/5);
-
- std::vector<float> features_orig(5 * 5);
- for (int i = 0; i < features_orig.size(); i++) {
- features_orig[i] = i;
- }
- VectorSpan<float> features;
-
- feature_extractor.SetV0FeatureMode(0);
- features = VectorSpan<float>(features_orig);
- feature_extractor.RemapV0FeatureVector(&features);
- EXPECT_EQ(
- std::vector<float>({0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,
- 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24}),
- std::vector<float>(features.begin(), features.end()));
-
- feature_extractor.SetV0FeatureMode(2);
- features = VectorSpan<float>(features_orig);
- feature_extractor.RemapV0FeatureVector(&features);
- EXPECT_EQ(std::vector<float>({0, 1, 5, 6, 10, 11, 15, 16, 20, 21, 2, 3, 4,
- 7, 8, 9, 12, 13, 14, 17, 18, 19, 22, 23, 24}),
- std::vector<float>(features.begin(), features.end()));
-}
-
-} // namespace
-} // namespace libtextclassifier
diff --git a/smartselect/model-params.cc b/smartselect/model-params.cc
deleted file mode 100644
index 65c4f93..0000000
--- a/smartselect/model-params.cc
+++ /dev/null
@@ -1,108 +0,0 @@
-/*
- * Copyright (C) 2017 The Android Open Source Project
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#include "smartselect/model-params.h"
-
-#include "common/memory_image/memory-image-reader.h"
-
-namespace libtextclassifier {
-
-using nlp_core::EmbeddingNetworkProto;
-using nlp_core::MemoryImageReader;
-
-ModelParams* ModelParamsBuilder(
- const void* start, uint64 num_bytes,
- std::shared_ptr<EmbeddingParams> external_embedding_params) {
- MemoryImageReader<EmbeddingNetworkProto> reader(start, num_bytes);
-
- ModelOptions model_options;
- auto model_options_extension_id = model_options_in_embedding_network_proto;
- if (reader.trimmed_proto().HasExtension(model_options_extension_id)) {
- model_options =
- reader.trimmed_proto().GetExtension(model_options_extension_id);
- }
-
- FeatureProcessorOptions feature_processor_options;
- auto feature_processor_extension_id =
- feature_processor_options_in_embedding_network_proto;
- if (reader.trimmed_proto().HasExtension(feature_processor_extension_id)) {
- feature_processor_options =
- reader.trimmed_proto().GetExtension(feature_processor_extension_id);
-
- // If no tokenization codepoint config is present, tokenize on space.
- // TODO(zilka): Remove the default config.
- if (feature_processor_options.tokenization_codepoint_config_size() == 0) {
- TokenizationCodepointRange* config;
- // New line character.
- config = feature_processor_options.add_tokenization_codepoint_config();
- config->set_start(10);
- config->set_end(11);
- config->set_role(TokenizationCodepointRange::WHITESPACE_SEPARATOR);
-
- // Space character.
- config = feature_processor_options.add_tokenization_codepoint_config();
- config->set_start(32);
- config->set_end(33);
- config->set_role(TokenizationCodepointRange::WHITESPACE_SEPARATOR);
- }
- } else {
- return nullptr;
- }
-
- SelectionModelOptions selection_options;
- auto selection_options_extension_id =
- selection_model_options_in_embedding_network_proto;
- if (reader.trimmed_proto().HasExtension(selection_options_extension_id)) {
- selection_options =
- reader.trimmed_proto().GetExtension(selection_options_extension_id);
-
- // For backward compatibility with the current models.
- if (!feature_processor_options.ignored_span_boundary_codepoints_size()) {
- *feature_processor_options.mutable_ignored_span_boundary_codepoints() =
- selection_options.deprecated_punctuation_to_strip();
- }
- } else {
- selection_options.set_enforce_symmetry(true);
- selection_options.set_symmetry_context_size(
- feature_processor_options.context_size() * 2);
- }
-
- SharingModelOptions sharing_options;
- auto sharing_options_extension_id =
- sharing_model_options_in_embedding_network_proto;
- if (reader.trimmed_proto().HasExtension(sharing_options_extension_id)) {
- sharing_options =
- reader.trimmed_proto().GetExtension(sharing_options_extension_id);
- } else {
- // Default values when SharingModelOptions is not present.
- sharing_options.set_always_accept_url_hint(true);
- sharing_options.set_always_accept_email_hint(true);
- }
-
- if (!model_options.use_shared_embeddings()) {
- std::shared_ptr<EmbeddingParams> embedding_params(new EmbeddingParams(
- start, num_bytes, feature_processor_options.context_size()));
- return new ModelParams(start, num_bytes, embedding_params,
- selection_options, sharing_options,
- feature_processor_options);
- } else {
- return new ModelParams(
- start, num_bytes, std::move(external_embedding_params),
- selection_options, sharing_options, feature_processor_options);
- }
-}
-
-} // namespace libtextclassifier
diff --git a/smartselect/model-params.h b/smartselect/model-params.h
deleted file mode 100644
index a0d39e6..0000000
--- a/smartselect/model-params.h
+++ /dev/null
@@ -1,152 +0,0 @@
-/*
- * Copyright (C) 2017 The Android Open Source Project
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-// Model parameter loading.
-
-#ifndef LIBTEXTCLASSIFIER_SMARTSELECT_MODEL_PARAMS_H_
-#define LIBTEXTCLASSIFIER_SMARTSELECT_MODEL_PARAMS_H_
-
-#include "common/embedding-network.h"
-#include "common/memory_image/embedding-network-params-from-image.h"
-#include "smartselect/text-classification-model.pb.h"
-
-namespace libtextclassifier {
-
-class EmbeddingParams : public nlp_core::EmbeddingNetworkParamsFromImage {
- public:
- EmbeddingParams(const void* start, uint64 num_bytes, int context_size)
- : EmbeddingNetworkParamsFromImage(start, num_bytes),
- context_size_(context_size) {}
-
- int embeddings_size() const override { return context_size_ * 2 + 1; }
-
- int embedding_num_features_size() const override {
- return context_size_ * 2 + 1;
- }
-
- int embedding_num_features(int i) const override { return 1; }
-
- int embeddings_num_rows(int i) const override {
- return EmbeddingNetworkParamsFromImage::embeddings_num_rows(0);
- };
-
- int embeddings_num_cols(int i) const override {
- return EmbeddingNetworkParamsFromImage::embeddings_num_cols(0);
- };
-
- const void* embeddings_weights(int i) const override {
- return EmbeddingNetworkParamsFromImage::embeddings_weights(0);
- };
-
- nlp_core::QuantizationType embeddings_quant_type(int i) const override {
- return EmbeddingNetworkParamsFromImage::embeddings_quant_type(0);
- }
-
- const nlp_core::float16* embeddings_quant_scales(int i) const override {
- return EmbeddingNetworkParamsFromImage::embeddings_quant_scales(0);
- }
-
- private:
- int context_size_;
-};
-
-// Loads and holds the parameters of the inference network.
-//
-// This class overrides a couple of methods of EmbeddingNetworkParamsFromImage
-// because we only have one embedding matrix for all positions of context,
-// whereas the original class would have a separate one for each.
-class ModelParams : public nlp_core::EmbeddingNetworkParamsFromImage {
- public:
- const FeatureProcessorOptions& GetFeatureProcessorOptions() const {
- return feature_processor_options_;
- }
-
- const SelectionModelOptions& GetSelectionModelOptions() const {
- return selection_options_;
- }
-
- const SharingModelOptions& GetSharingModelOptions() const {
- return sharing_options_;
- }
-
- std::shared_ptr<EmbeddingParams> GetEmbeddingParams() const {
- return embedding_params_;
- }
-
- protected:
- int embeddings_size() const override {
- return embedding_params_->embeddings_size();
- }
-
- int embedding_num_features_size() const override {
- return embedding_params_->embedding_num_features_size();
- }
-
- int embedding_num_features(int i) const override {
- return embedding_params_->embedding_num_features(i);
- }
-
- int embeddings_num_rows(int i) const override {
- return embedding_params_->embeddings_num_rows(i);
- };
-
- int embeddings_num_cols(int i) const override {
- return embedding_params_->embeddings_num_cols(i);
- };
-
- const void* embeddings_weights(int i) const override {
- return embedding_params_->embeddings_weights(i);
- };
-
- nlp_core::QuantizationType embeddings_quant_type(int i) const override {
- return embedding_params_->embeddings_quant_type(i);
- }
-
- const nlp_core::float16* embeddings_quant_scales(int i) const override {
- return embedding_params_->embeddings_quant_scales(i);
- }
-
- private:
- friend ModelParams* ModelParamsBuilder(
- const void* start, uint64 num_bytes,
- std::shared_ptr<EmbeddingParams> external_embedding_params);
-
- ModelParams(const void* start, uint64 num_bytes,
- std::shared_ptr<EmbeddingParams> embedding_params,
- const SelectionModelOptions& selection_options,
- const SharingModelOptions& sharing_options,
- const FeatureProcessorOptions& feature_processor_options)
- : EmbeddingNetworkParamsFromImage(start, num_bytes),
- selection_options_(selection_options),
- sharing_options_(sharing_options),
- feature_processor_options_(feature_processor_options),
- context_size_(feature_processor_options_.context_size()),
- embedding_params_(std::move(embedding_params)) {}
-
- SelectionModelOptions selection_options_;
- SharingModelOptions sharing_options_;
- FeatureProcessorOptions feature_processor_options_;
- int context_size_;
- std::shared_ptr<EmbeddingParams> embedding_params_;
-};
-
-ModelParams* ModelParamsBuilder(
- const void* start, uint64 num_bytes,
- std::shared_ptr<EmbeddingParams> external_embedding_params);
-
-} // namespace libtextclassifier
-
-#endif // LIBTEXTCLASSIFIER_SMARTSELECT_MODEL_PARAMS_H_
diff --git a/smartselect/model-parser.cc b/smartselect/model-parser.cc
deleted file mode 100644
index 0cf05e3..0000000
--- a/smartselect/model-parser.cc
+++ /dev/null
@@ -1,91 +0,0 @@
-/*
- * Copyright (C) 2017 The Android Open Source Project
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#include "smartselect/model-parser.h"
-#include "util/base/endian.h"
-
-namespace libtextclassifier {
-namespace {
-
-// Small helper class for parsing the merged model format.
-// The merged model consists of interleaved <int32 data_size, char* data>
-// segments.
-class MergedModelParser {
- public:
- MergedModelParser(const void* addr, const int size)
- : addr_(reinterpret_cast<const char*>(addr)), size_(size), pos_(addr_) {}
-
- bool ReadBytesAndAdvance(int num_bytes, const char** result) {
- const char* read_addr = pos_;
- if (Advance(num_bytes)) {
- *result = read_addr;
- return true;
- } else {
- return false;
- }
- }
-
- bool ReadInt32AndAdvance(int* result) {
- const char* read_addr = pos_;
- if (Advance(sizeof(int))) {
- *result =
- LittleEndian::ToHost32(*reinterpret_cast<const uint32*>(read_addr));
- return true;
- } else {
- return false;
- }
- }
-
- bool IsDone() { return pos_ == addr_ + size_; }
-
- private:
- bool Advance(int num_bytes) {
- pos_ += num_bytes;
- return pos_ <= addr_ + size_;
- }
-
- const char* addr_;
- const int size_;
- const char* pos_;
-};
-
-} // namespace
-
-bool ParseMergedModel(const void* addr, const int size,
- const char** selection_model, int* selection_model_length,
- const char** sharing_model, int* sharing_model_length) {
- MergedModelParser parser(addr, size);
-
- if (!parser.ReadInt32AndAdvance(selection_model_length)) {
- return false;
- }
-
- if (!parser.ReadBytesAndAdvance(*selection_model_length, selection_model)) {
- return false;
- }
-
- if (!parser.ReadInt32AndAdvance(sharing_model_length)) {
- return false;
- }
-
- if (!parser.ReadBytesAndAdvance(*sharing_model_length, sharing_model)) {
- return false;
- }
-
- return parser.IsDone();
-}
-
-} // namespace libtextclassifier
diff --git a/smartselect/model-parser.h b/smartselect/model-parser.h
deleted file mode 100644
index 801262f..0000000
--- a/smartselect/model-parser.h
+++ /dev/null
@@ -1,29 +0,0 @@
-/*
- * Copyright (C) 2017 The Android Open Source Project
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#ifndef LIBTEXTCLASSIFIER_SMARTSELECT_MODEL_PARSER_H_
-#define LIBTEXTCLASSIFIER_SMARTSELECT_MODEL_PARSER_H_
-
-namespace libtextclassifier {
-
-// Parse a merged model image.
-bool ParseMergedModel(const void* addr, const int size,
- const char** selection_model, int* selection_model_length,
- const char** sharing_model, int* sharing_model_length);
-
-} // namespace libtextclassifier
-
-#endif // LIBTEXTCLASSIFIER_SMARTSELECT_MODEL_PARSER_H_
diff --git a/smartselect/text-classification-model.cc b/smartselect/text-classification-model.cc
deleted file mode 100644
index e7ae09c..0000000
--- a/smartselect/text-classification-model.cc
+++ /dev/null
@@ -1,741 +0,0 @@
-/*
- * Copyright (C) 2017 The Android Open Source Project
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#include "smartselect/text-classification-model.h"
-
-#include <cctype>
-#include <cmath>
-#include <iterator>
-#include <numeric>
-
-#include "common/embedding-network.h"
-#include "common/feature-extractor.h"
-#include "common/memory_image/embedding-network-params-from-image.h"
-#include "common/memory_image/memory-image-reader.h"
-#include "common/mmap.h"
-#include "common/softmax.h"
-#include "smartselect/model-parser.h"
-#include "smartselect/text-classification-model.pb.h"
-#include "util/base/logging.h"
-#include "util/utf8/unicodetext.h"
-#ifndef LIBTEXTCLASSIFIER_DISABLE_ICU_SUPPORT
-#include "unicode/regex.h"
-#include "unicode/uchar.h"
-#endif
-
-namespace libtextclassifier {
-
-using nlp_core::EmbeddingNetwork;
-using nlp_core::EmbeddingNetworkProto;
-using nlp_core::FeatureVector;
-using nlp_core::MemoryImageReader;
-using nlp_core::MmapFile;
-using nlp_core::MmapHandle;
-using nlp_core::ScopedMmap;
-
-namespace {
-
-int CountDigits(const std::string& str, CodepointSpan selection_indices) {
- int count = 0;
- int i = 0;
- const UnicodeText unicode_str = UTF8ToUnicodeText(str, /*do_copy=*/false);
- for (auto it = unicode_str.begin(); it != unicode_str.end(); ++it, ++i) {
- if (i >= selection_indices.first && i < selection_indices.second &&
- isdigit(*it)) {
- ++count;
- }
- }
- return count;
-}
-
-std::string ExtractSelection(const std::string& context,
- CodepointSpan selection_indices) {
- const UnicodeText context_unicode =
- UTF8ToUnicodeText(context, /*do_copy=*/false);
- auto selection_begin = context_unicode.begin();
- std::advance(selection_begin, selection_indices.first);
- auto selection_end = context_unicode.begin();
- std::advance(selection_end, selection_indices.second);
- return UnicodeText::UTF8Substring(selection_begin, selection_end);
-}
-
-#ifndef LIBTEXTCLASSIFIER_DISABLE_ICU_SUPPORT
-bool MatchesRegex(const icu::RegexPattern* regex, const std::string& context) {
- const icu::UnicodeString unicode_context(context.c_str(), context.size(),
- "utf-8");
- UErrorCode status = U_ZERO_ERROR;
- std::unique_ptr<icu::RegexMatcher> matcher(
- regex->matcher(unicode_context, status));
- return matcher->matches(0 /* start */, status);
-}
-#endif
-
-} // namespace
-
-TextClassificationModel::TextClassificationModel(const std::string& path)
- : mmap_(new nlp_core::ScopedMmap(path)) {
- InitFromMmap();
-}
-
-TextClassificationModel::TextClassificationModel(int fd)
- : mmap_(new nlp_core::ScopedMmap(fd)) {
- InitFromMmap();
-}
-
-TextClassificationModel::TextClassificationModel(int fd, int offset, int size)
- : mmap_(new nlp_core::ScopedMmap(fd, offset, size)) {
- InitFromMmap();
-}
-
-TextClassificationModel::TextClassificationModel(const void* addr, int size) {
- initialized_ = LoadModels(addr, size);
- if (!initialized_) {
- TC_LOG(ERROR) << "Failed to load models";
- return;
- }
-}
-
-void TextClassificationModel::InitFromMmap() {
- if (!mmap_->handle().ok()) {
- return;
- }
-
- initialized_ =
- LoadModels(mmap_->handle().start(), mmap_->handle().num_bytes());
- if (!initialized_) {
- TC_LOG(ERROR) << "Failed to load models";
- return;
- }
-}
-
-namespace {
-
-// Converts sparse features vector to nlp_core::FeatureVector.
-void SparseFeaturesToFeatureVector(
- const std::vector<int> sparse_features,
- const nlp_core::NumericFeatureType& feature_type,
- nlp_core::FeatureVector* result) {
- for (int feature_id : sparse_features) {
- const int64 feature_value =
- nlp_core::FloatFeatureValue(feature_id, 1.0 / sparse_features.size())
- .discrete_value;
- result->add(const_cast<nlp_core::NumericFeatureType*>(&feature_type),
- feature_value);
- }
-}
-
-// Returns a function that can be used for mapping sparse and dense features
-// to a float feature vector.
-// NOTE: The network object needs to be available at the time when the returned
-// function object is used.
-FeatureVectorFn CreateFeatureVectorFn(const EmbeddingNetwork& network,
- int sparse_embedding_size) {
- const nlp_core::NumericFeatureType feature_type("chargram_continuous", 0);
- return [&network, sparse_embedding_size, feature_type](
- const std::vector<int>& sparse_features,
- const std::vector<float>& dense_features, float* embedding) {
- nlp_core::FeatureVector feature_vector;
- SparseFeaturesToFeatureVector(sparse_features, feature_type,
- &feature_vector);
-
- if (network.GetEmbedding(feature_vector, 0, embedding)) {
- for (int i = 0; i < dense_features.size(); i++) {
- embedding[sparse_embedding_size + i] = dense_features[i];
- }
- return true;
- } else {
- return false;
- }
- };
-}
-
-} // namespace
-
-void TextClassificationModel::InitializeSharingRegexPatterns(
- const std::vector<SharingModelOptions::RegexPattern>& patterns) {
-#ifndef LIBTEXTCLASSIFIER_DISABLE_ICU_SUPPORT
- // Initialize pattern recognizers.
- for (const auto& regex_pattern : patterns) {
- UErrorCode status = U_ZERO_ERROR;
- std::unique_ptr<icu::RegexPattern> compiled_pattern(
- icu::RegexPattern::compile(
- icu::UnicodeString(regex_pattern.pattern().c_str(),
- regex_pattern.pattern().size(), "utf-8"),
- 0 /* flags */, status));
- if (U_FAILURE(status)) {
- TC_LOG(WARNING) << "Failed to load pattern" << regex_pattern.pattern();
- } else {
- regex_patterns_.push_back(
- {regex_pattern.collection_name(), std::move(compiled_pattern)});
- }
- }
-#else
- if (!patterns.empty()) {
- TC_LOG(WARNING) << "ICU not supported regexp matchers ignored.";
- }
-#endif
-}
-
-bool TextClassificationModel::LoadModels(const void* addr, int size) {
- const char *selection_model, *sharing_model;
- int selection_model_length, sharing_model_length;
- if (!ParseMergedModel(addr, size, &selection_model, &selection_model_length,
- &sharing_model, &sharing_model_length)) {
- TC_LOG(ERROR) << "Couldn't parse the model.";
- return false;
- }
-
- selection_params_.reset(
- ModelParamsBuilder(selection_model, selection_model_length, nullptr));
- if (!selection_params_.get()) {
- return false;
- }
- selection_options_ = selection_params_->GetSelectionModelOptions();
- selection_network_.reset(new EmbeddingNetwork(selection_params_.get()));
- selection_feature_processor_.reset(
- new FeatureProcessor(selection_params_->GetFeatureProcessorOptions()));
- selection_feature_fn_ = CreateFeatureVectorFn(
- *selection_network_, selection_network_->EmbeddingSize(0));
-
- sharing_params_.reset(
- ModelParamsBuilder(sharing_model, sharing_model_length,
- selection_params_->GetEmbeddingParams()));
- if (!sharing_params_.get()) {
- return false;
- }
- sharing_options_ = selection_params_->GetSharingModelOptions();
- sharing_network_.reset(new EmbeddingNetwork(sharing_params_.get()));
- sharing_feature_processor_.reset(
- new FeatureProcessor(sharing_params_->GetFeatureProcessorOptions()));
- sharing_feature_fn_ = CreateFeatureVectorFn(
- *sharing_network_, sharing_network_->EmbeddingSize(0));
-
- InitializeSharingRegexPatterns(std::vector<SharingModelOptions::RegexPattern>(
- sharing_options_.regex_pattern().begin(),
- sharing_options_.regex_pattern().end()));
-
- return true;
-}
-
-bool ReadSelectionModelOptions(int fd, ModelOptions* model_options) {
- ScopedMmap mmap = ScopedMmap(fd);
- if (!mmap.handle().ok()) {
- TC_LOG(ERROR) << "Can't mmap.";
- return false;
- }
-
- const char *selection_model, *sharing_model;
- int selection_model_length, sharing_model_length;
- if (!ParseMergedModel(mmap.handle().start(), mmap.handle().num_bytes(),
- &selection_model, &selection_model_length,
- &sharing_model, &sharing_model_length)) {
- TC_LOG(ERROR) << "Couldn't parse merged model.";
- return false;
- }
-
- MemoryImageReader<EmbeddingNetworkProto> reader(selection_model,
- selection_model_length);
-
- auto model_options_extension_id = model_options_in_embedding_network_proto;
- if (reader.trimmed_proto().HasExtension(model_options_extension_id)) {
- *model_options =
- reader.trimmed_proto().GetExtension(model_options_extension_id);
- return true;
- } else {
- return false;
- }
-}
-
-EmbeddingNetwork::Vector TextClassificationModel::InferInternal(
- const std::string& context, CodepointSpan span,
- const FeatureProcessor& feature_processor, const EmbeddingNetwork& network,
- const FeatureVectorFn& feature_vector_fn,
- std::vector<CodepointSpan>* selection_label_spans) const {
- std::vector<Token> tokens;
- int click_pos;
- std::unique_ptr<CachedFeatures> cached_features;
- const int embedding_size = network.EmbeddingSize(0);
- if (!feature_processor.ExtractFeatures(
- context, span, /*relative_click_span=*/{0, 0},
- CreateFeatureVectorFn(network, embedding_size),
- embedding_size + feature_processor.DenseFeaturesCount(), &tokens,
- &click_pos, &cached_features)) {
- TC_VLOG(1) << "Could not extract features.";
- return {};
- }
-
- VectorSpan<float> features;
- VectorSpan<Token> output_tokens;
- if (!cached_features->Get(click_pos, &features, &output_tokens)) {
- TC_VLOG(1) << "Could not extract features.";
- return {};
- }
-
- if (selection_label_spans != nullptr) {
- if (!feature_processor.SelectionLabelSpans(output_tokens,
- selection_label_spans)) {
- TC_LOG(ERROR) << "Could not get spans for selection labels.";
- return {};
- }
- }
-
- std::vector<float> scores;
- network.ComputeLogits(features, &scores);
- return scores;
-}
-
-namespace {
-
-// Returns true if given codepoint is contained in the given span in context.
-bool IsCodepointInSpan(const char32 codepoint, const std::string& context,
- const CodepointSpan span) {
- const UnicodeText context_unicode =
- UTF8ToUnicodeText(context, /*do_copy=*/false);
-
- auto begin_it = context_unicode.begin();
- std::advance(begin_it, span.first);
- auto end_it = context_unicode.begin();
- std::advance(end_it, span.second);
-
- return std::find(begin_it, end_it, codepoint) != end_it;
-}
-
-// Returns the first codepoint of the span.
-char32 FirstSpanCodepoint(const std::string& context,
- const CodepointSpan span) {
- const UnicodeText context_unicode =
- UTF8ToUnicodeText(context, /*do_copy=*/false);
-
- auto it = context_unicode.begin();
- std::advance(it, span.first);
- return *it;
-}
-
-// Returns the last codepoint of the span.
-char32 LastSpanCodepoint(const std::string& context, const CodepointSpan span) {
- const UnicodeText context_unicode =
- UTF8ToUnicodeText(context, /*do_copy=*/false);
-
- auto it = context_unicode.begin();
- std::advance(it, span.second - 1);
- return *it;
-}
-
-} // namespace
-
-#ifndef LIBTEXTCLASSIFIER_DISABLE_ICU_SUPPORT
-
-namespace {
-
-bool IsOpenBracket(const char32 codepoint) {
- return u_getIntPropertyValue(codepoint, UCHAR_BIDI_PAIRED_BRACKET_TYPE) ==
- U_BPT_OPEN;
-}
-
-bool IsClosingBracket(const char32 codepoint) {
- return u_getIntPropertyValue(codepoint, UCHAR_BIDI_PAIRED_BRACKET_TYPE) ==
- U_BPT_CLOSE;
-}
-
-} // namespace
-
-// If the first or the last codepoint of the given span is a bracket, the
-// bracket is stripped if the span does not contain its corresponding paired
-// version.
-CodepointSpan StripUnpairedBrackets(const std::string& context,
- CodepointSpan span) {
- if (context.empty()) {
- return span;
- }
-
- const char32 begin_char = FirstSpanCodepoint(context, span);
-
- const char32 paired_begin_char = u_getBidiPairedBracket(begin_char);
- if (paired_begin_char != begin_char) {
- if (!IsOpenBracket(begin_char) ||
- !IsCodepointInSpan(paired_begin_char, context, span)) {
- ++span.first;
- }
- }
-
- if (span.first == span.second) {
- return span;
- }
-
- const char32 end_char = LastSpanCodepoint(context, span);
- const char32 paired_end_char = u_getBidiPairedBracket(end_char);
- if (paired_end_char != end_char) {
- if (!IsClosingBracket(end_char) ||
- !IsCodepointInSpan(paired_end_char, context, span)) {
- --span.second;
- }
- }
-
- // Should not happen, but let's make sure.
- if (span.first > span.second) {
- TC_LOG(WARNING) << "Inverse indices result: " << span.first << ", "
- << span.second;
- span.second = span.first;
- }
-
- return span;
-}
-#endif
-
-CodepointSpan TextClassificationModel::SuggestSelection(
- const std::string& context, CodepointSpan click_indices) const {
- if (!initialized_) {
- TC_LOG(ERROR) << "Not initialized";
- return click_indices;
- }
-
- const int context_codepoint_size =
- UTF8ToUnicodeText(context, /*do_copy=*/false).size();
-
- 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) {
- TC_VLOG(1) << "Trying to run SuggestSelection with invalid indices: "
- << click_indices.first << " " << click_indices.second;
- return click_indices;
- }
-
- CodepointSpan result;
- if (selection_options_.enforce_symmetry()) {
- result = SuggestSelectionSymmetrical(context, click_indices);
- } else {
- float score;
- std::tie(result, score) = SuggestSelectionInternal(context, click_indices);
- }
-
-#ifndef LIBTEXTCLASSIFIER_DISABLE_ICU_SUPPORT
- if (selection_options_.strip_unpaired_brackets()) {
- const CodepointSpan stripped_result =
- StripUnpairedBrackets(context, result);
- if (stripped_result.first != stripped_result.second) {
- result = stripped_result;
- }
- }
-#endif
-
- return result;
-}
-
-namespace {
-
-int BestPrediction(const std::vector<float>& scores) {
- if (!scores.empty()) {
- const int prediction =
- std::max_element(scores.begin(), scores.end()) - scores.begin();
- return prediction;
- } else {
- return kInvalidLabel;
- }
-}
-
-std::pair<CodepointSpan, float> BestSelectionSpan(
- CodepointSpan original_click_indices, const std::vector<float>& scores,
- const std::vector<CodepointSpan>& selection_label_spans) {
- const int prediction = BestPrediction(scores);
- if (prediction != kInvalidLabel) {
- std::pair<CodepointIndex, CodepointIndex> selection =
- selection_label_spans[prediction];
-
- if (selection.first == kInvalidIndex || selection.second == kInvalidIndex) {
- TC_VLOG(1) << "Invalid indices predicted, returning input: " << prediction
- << " " << selection.first << " " << selection.second;
- return {original_click_indices, -1.0};
- }
-
- return {{selection.first, selection.second}, scores[prediction]};
- } else {
- TC_LOG(ERROR) << "Returning default selection: scores.size() = "
- << scores.size();
- return {original_click_indices, -1.0};
- }
-}
-
-} // namespace
-
-std::pair<CodepointSpan, float>
-TextClassificationModel::SuggestSelectionInternal(
- const std::string& context, CodepointSpan click_indices) const {
- if (!initialized_) {
- TC_LOG(ERROR) << "Not initialized";
- return {click_indices, -1.0};
- }
-
- std::vector<CodepointSpan> selection_label_spans;
- EmbeddingNetwork::Vector scores = InferInternal(
- context, click_indices, *selection_feature_processor_,
- *selection_network_, selection_feature_fn_, &selection_label_spans);
- scores = nlp_core::ComputeSoftmax(scores);
-
- return BestSelectionSpan(click_indices, scores, selection_label_spans);
-}
-
-// Implements a greedy-search-like algorithm for making selections symmetric.
-//
-// Steps:
-// 1. Get a set of selection proposals from places around the clicked word.
-// 2. For each proposal (going from highest-scoring), check if the tokens that
-// the proposal selects are still free, in which case it claims them, if a
-// proposal that contains the clicked token is found, it is returned as the
-// suggestion.
-//
-// This algorithm should ensure that if a selection is proposed, it does not
-// matter which word of it was tapped - all of them will lead to the same
-// selection.
-CodepointSpan TextClassificationModel::SuggestSelectionSymmetrical(
- const std::string& context, CodepointSpan click_indices) const {
- const int symmetry_context_size = selection_options_.symmetry_context_size();
- std::vector<CodepointSpan> chunks = Chunk(
- context, click_indices, {symmetry_context_size, symmetry_context_size});
- for (const CodepointSpan& chunk : chunks) {
- // If chunk and click indices have an overlap, return the chunk.
- if (!(click_indices.first >= chunk.second ||
- click_indices.second <= chunk.first)) {
- return chunk;
- }
- }
-
- return click_indices;
-}
-
-std::vector<std::pair<std::string, float>>
-TextClassificationModel::ClassifyText(const std::string& context,
- CodepointSpan selection_indices,
- int hint_flags) const {
- if (!initialized_) {
- TC_LOG(ERROR) << "Not initialized";
- return {};
- }
-
- if (std::get<0>(selection_indices) >= std::get<1>(selection_indices)) {
- TC_VLOG(1) << "Trying to run ClassifyText with invalid indices: "
- << std::get<0>(selection_indices) << " "
- << std::get<1>(selection_indices);
- return {};
- }
-
- if (hint_flags & SELECTION_IS_URL &&
- sharing_options_.always_accept_url_hint()) {
- return {{kUrlHintCollection, 1.0}};
- }
-
- if (hint_flags & SELECTION_IS_EMAIL &&
- sharing_options_.always_accept_email_hint()) {
- return {{kEmailHintCollection, 1.0}};
- }
-
- // Check whether any of the regular expressions match.
-#ifndef LIBTEXTCLASSIFIER_DISABLE_ICU_SUPPORT
- const std::string selection_text =
- ExtractSelection(context, selection_indices);
- for (const CompiledRegexPattern& regex_pattern : regex_patterns_) {
- if (MatchesRegex(regex_pattern.pattern.get(), selection_text)) {
- return {{regex_pattern.collection_name, 1.0}};
- }
- }
-#endif
-
- EmbeddingNetwork::Vector scores =
- InferInternal(context, selection_indices, *sharing_feature_processor_,
- *sharing_network_, sharing_feature_fn_, nullptr);
- if (scores.empty() ||
- scores.size() != sharing_feature_processor_->NumCollections()) {
- TC_VLOG(1) << "Using default class: scores.size() = " << scores.size();
- return {};
- }
-
- scores = nlp_core::ComputeSoftmax(scores);
-
- std::vector<std::pair<std::string, float>> result(scores.size());
- for (int i = 0; i < scores.size(); i++) {
- result[i] = {sharing_feature_processor_->LabelToCollection(i), scores[i]};
- }
- std::sort(result.begin(), result.end(),
- [](const std::pair<std::string, float>& a,
- const std::pair<std::string, float>& b) {
- return a.second > b.second;
- });
-
- // Phone class sanity check.
- if (result.begin()->first == kPhoneCollection) {
- const int digit_count = CountDigits(context, selection_indices);
- if (digit_count < sharing_options_.phone_min_num_digits() ||
- digit_count > sharing_options_.phone_max_num_digits()) {
- return {{kOtherCollection, 1.0}};
- }
- }
-
- return result;
-}
-
-std::vector<CodepointSpan> TextClassificationModel::Chunk(
- const std::string& context, CodepointSpan click_span,
- TokenSpan relative_click_span) const {
- std::unique_ptr<CachedFeatures> cached_features;
- std::vector<Token> tokens;
- int click_index;
- int embedding_size = selection_network_->EmbeddingSize(0);
- if (!selection_feature_processor_->ExtractFeatures(
- context, click_span, relative_click_span, selection_feature_fn_,
- embedding_size + selection_feature_processor_->DenseFeaturesCount(),
- &tokens, &click_index, &cached_features)) {
- TC_VLOG(1) << "Couldn't ExtractFeatures.";
- return {};
- }
-
- int first_token;
- int last_token;
- if (relative_click_span.first == kInvalidIndex ||
- relative_click_span.second == kInvalidIndex) {
- first_token = 0;
- last_token = tokens.size();
- } else {
- first_token = click_index - relative_click_span.first;
- last_token = click_index + relative_click_span.second + 1;
- }
-
- struct SelectionProposal {
- int label;
- int token_index;
- CodepointSpan span;
- float score;
- };
-
- // Scan in the symmetry context for selection span proposals.
- std::vector<SelectionProposal> proposals;
- for (int token_index = first_token; token_index < last_token; ++token_index) {
- if (token_index < 0 || token_index >= tokens.size() ||
- tokens[token_index].is_padding) {
- continue;
- }
-
- float score;
- VectorSpan<float> features;
- VectorSpan<Token> output_tokens;
- std::vector<CodepointSpan> selection_label_spans;
- CodepointSpan span;
- if (cached_features->Get(token_index, &features, &output_tokens) &&
- selection_feature_processor_->SelectionLabelSpans(
- output_tokens, &selection_label_spans)) {
- // Add an implicit proposal for each token to be by itself. Every
- // token should be now represented in the results.
- proposals.push_back(
- SelectionProposal{0, token_index, selection_label_spans[0], 0.0});
-
- std::vector<float> scores;
- selection_network_->ComputeLogits(features, &scores);
-
- scores = nlp_core::ComputeSoftmax(scores);
- std::tie(span, score) = BestSelectionSpan({kInvalidIndex, kInvalidIndex},
- scores, selection_label_spans);
- if (span.first != kInvalidIndex && span.second != kInvalidIndex &&
- score >= 0) {
- const int prediction = BestPrediction(scores);
- proposals.push_back(
- SelectionProposal{prediction, token_index, span, score});
- }
- } else {
- // Add an implicit proposal for each token to be by itself. Every token
- // should be now represented in the results.
- proposals.push_back(SelectionProposal{
- 0,
- token_index,
- {tokens[token_index].start, tokens[token_index].end},
- 0.0});
- }
- }
-
- // Sort selection span proposals by their respective probabilities.
- std::sort(proposals.begin(), proposals.end(),
- [](const SelectionProposal& a, const SelectionProposal& b) {
- return a.score > b.score;
- });
-
- // Go from the highest-scoring proposal and claim tokens. Tokens are marked as
- // claimed by the higher-scoring selection proposals, so that the
- // lower-scoring ones cannot use them. Returns the selection proposal if it
- // contains the clicked token.
- std::vector<CodepointSpan> result;
- std::vector<bool> token_used(tokens.size(), false);
- for (const SelectionProposal& proposal : proposals) {
- const int predicted_label = proposal.label;
- TokenSpan relative_span;
- if (!selection_feature_processor_->LabelToTokenSpan(predicted_label,
- &relative_span)) {
- continue;
- }
- TokenSpan span;
- span.first = proposal.token_index - relative_span.first;
- span.second = proposal.token_index + relative_span.second + 1;
-
- if (span.first != kInvalidIndex && span.second != kInvalidIndex) {
- bool feasible = true;
- for (int i = span.first; i < span.second; i++) {
- if (token_used[i]) {
- feasible = false;
- break;
- }
- }
-
- if (feasible) {
- result.push_back(proposal.span);
- for (int i = span.first; i < span.second; i++) {
- token_used[i] = true;
- }
- }
- }
- }
-
- std::sort(result.begin(), result.end(),
- [](const CodepointSpan& a, const CodepointSpan& b) {
- return a.first < b.first;
- });
-
- return result;
-}
-
-std::vector<TextClassificationModel::AnnotatedSpan>
-TextClassificationModel::Annotate(const std::string& context) const {
- std::vector<CodepointSpan> chunks;
- const UnicodeText context_unicode = UTF8ToUnicodeText(context,
- /*do_copy=*/false);
- for (const UnicodeTextRange& line :
- selection_feature_processor_->SplitContext(context_unicode)) {
- const std::vector<CodepointSpan> local_chunks =
- Chunk(UnicodeText::UTF8Substring(line.first, line.second),
- /*click_span=*/{kInvalidIndex, kInvalidIndex},
- /*relative_click_span=*/{kInvalidIndex, kInvalidIndex});
- const int offset = std::distance(context_unicode.begin(), line.first);
- for (CodepointSpan chunk : local_chunks) {
- chunks.push_back({chunk.first + offset, chunk.second + offset});
- }
- }
-
- std::vector<TextClassificationModel::AnnotatedSpan> result;
- for (const CodepointSpan& chunk : chunks) {
- result.emplace_back();
- result.back().span = chunk;
- result.back().classification = ClassifyText(context, chunk);
- }
- return result;
-}
-
-} // namespace libtextclassifier
diff --git a/smartselect/text-classification-model.h b/smartselect/text-classification-model.h
deleted file mode 100644
index d0df193..0000000
--- a/smartselect/text-classification-model.h
+++ /dev/null
@@ -1,196 +0,0 @@
-/*
- * Copyright (C) 2017 The Android Open Source Project
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-// Inference code for the feed-forward text classification models.
-
-#ifndef LIBTEXTCLASSIFIER_SMARTSELECT_TEXT_CLASSIFICATION_MODEL_H_
-#define LIBTEXTCLASSIFIER_SMARTSELECT_TEXT_CLASSIFICATION_MODEL_H_
-
-#include <memory>
-#include <set>
-#include <string>
-
-#include "common/embedding-network.h"
-#include "common/feature-extractor.h"
-#include "common/memory_image/embedding-network-params-from-image.h"
-#include "common/mmap.h"
-#include "smartselect/feature-processor.h"
-#include "smartselect/model-params.h"
-#include "smartselect/text-classification-model.pb.h"
-#include "smartselect/types.h"
-
-namespace libtextclassifier {
-
-// SmartSelection/Sharing feed-forward model.
-class TextClassificationModel {
- public:
- // Represents a result of Annotate call.
- struct AnnotatedSpan {
- // Unicode codepoint indices in the input string.
- CodepointSpan span = {kInvalidIndex, kInvalidIndex};
-
- // Classification result for the span.
- std::vector<std::pair<std::string, float>> classification;
- };
-
- // Loads TextClassificationModel from given file given by an int
- // file descriptor.
- // Offset is byte a position in the file to the beginning of the model data.
- TextClassificationModel(int fd, int offset, int size);
-
- // Same as above but the whole file is mapped and it is assumed the model
- // starts at offset 0.
- explicit TextClassificationModel(int fd);
-
- // Loads TextClassificationModel from given file.
- explicit TextClassificationModel(const std::string& path);
-
- // Loads TextClassificationModel from given location in memory.
- TextClassificationModel(const void* addr, int size);
-
- // Returns true if the model is ready for use.
- bool IsInitialized() { return initialized_; }
-
- // Bit flags for the input selection.
- enum SelectionInputFlags { SELECTION_IS_URL = 0x1, SELECTION_IS_EMAIL = 0x2 };
-
- // Runs inference for given a context and current selection (i.e. index
- // of the first and one past last selected characters (utf8 codepoint
- // offsets)). Returns the indices (utf8 codepoint offsets) of the selection
- // beginning character and one past selection end character.
- // Returns the original click_indices if an error occurs.
- // NOTE: The selection indices are passed in and returned in terms of
- // UTF8 codepoints (not bytes).
- // Requires that the model is a smart selection model.
- CodepointSpan SuggestSelection(const std::string& context,
- CodepointSpan click_indices) const;
-
- // Classifies the selected text given the context string.
- // Requires that the model is a smart sharing model.
- // Returns an empty result if an error occurs.
- std::vector<std::pair<std::string, float>> ClassifyText(
- const std::string& context, CodepointSpan click_indices,
- int input_flags = 0) const;
-
- // Annotates given input text. The annotations should cover the whole input
- // context except for whitespaces, and are sorted by their position in the
- // context string.
- std::vector<AnnotatedSpan> Annotate(const std::string& context) const;
-
- protected:
- // Initializes the model from mmap_ file.
- void InitFromMmap();
-
- // Extracts chunks from the context. The extraction proceeds from the center
- // token determined by click_span and looks at relative_click_span tokens
- // left and right around the click position.
- // If relative_click_span == {kInvalidIndex, kInvalidIndex} then the whole
- // context is considered, regardless of the click_span.
- // Returns the chunks sorted by their position in the context string.
- std::vector<CodepointSpan> Chunk(const std::string& context,
- CodepointSpan click_span,
- TokenSpan relative_click_span) const;
-
- // During evaluation we need access to the feature processor.
- FeatureProcessor* SelectionFeatureProcessor() const {
- return selection_feature_processor_.get();
- }
-
- void InitializeSharingRegexPatterns(
- const std::vector<SharingModelOptions::RegexPattern>& patterns);
-
- // Collection name when url hint is accepted.
- const std::string kUrlHintCollection = "url";
-
- // Collection name when email hint is accepted.
- const std::string kEmailHintCollection = "email";
-
- // Collection name for other.
- const std::string kOtherCollection = "other";
-
- // Collection name for phone.
- const std::string kPhoneCollection = "phone";
-
- SelectionModelOptions selection_options_;
- SharingModelOptions sharing_options_;
-
- private:
-#ifndef LIBTEXTCLASSIFIER_DISABLE_ICU_SUPPORT
- struct CompiledRegexPattern {
- std::string collection_name;
- std::unique_ptr<icu::RegexPattern> pattern;
- };
-#endif
-
- bool LoadModels(const void* addr, int size);
-
- nlp_core::EmbeddingNetwork::Vector InferInternal(
- const std::string& context, CodepointSpan span,
- const FeatureProcessor& feature_processor,
- const nlp_core::EmbeddingNetwork& network,
- const FeatureVectorFn& feature_vector_fn,
- std::vector<CodepointSpan>* selection_label_spans) const;
-
- // Returns a selection suggestion with a score.
- std::pair<CodepointSpan, float> SuggestSelectionInternal(
- const std::string& context, CodepointSpan click_indices) const;
-
- // Returns a selection suggestion and makes sure it's symmetric. Internally
- // runs several times SuggestSelectionInternal.
- CodepointSpan SuggestSelectionSymmetrical(const std::string& context,
- CodepointSpan click_indices) const;
-
- bool initialized_ = false;
- std::unique_ptr<nlp_core::ScopedMmap> mmap_;
- std::unique_ptr<ModelParams> selection_params_;
- std::unique_ptr<FeatureProcessor> selection_feature_processor_;
- std::unique_ptr<nlp_core::EmbeddingNetwork> selection_network_;
- FeatureVectorFn selection_feature_fn_;
- std::unique_ptr<FeatureProcessor> sharing_feature_processor_;
- std::unique_ptr<ModelParams> sharing_params_;
- std::unique_ptr<nlp_core::EmbeddingNetwork> sharing_network_;
- FeatureVectorFn sharing_feature_fn_;
-#ifndef LIBTEXTCLASSIFIER_DISABLE_ICU_SUPPORT
- std::vector<CompiledRegexPattern> regex_patterns_;
-#endif
-};
-
-// If the first or the last codepoint of the given span is a bracket, the
-// bracket is stripped if the span does not contain its corresponding paired
-// version.
-CodepointSpan StripUnpairedBrackets(const std::string& context,
- CodepointSpan span);
-
-// Parses the merged image given as a file descriptor, and reads
-// the ModelOptions proto from the selection model.
-bool ReadSelectionModelOptions(int fd, ModelOptions* model_options);
-
-// Pretty-printing function for TextClassificationModel::AnnotatedSpan.
-inline std::ostream& operator<<(
- std::ostream& os, const TextClassificationModel::AnnotatedSpan& span) {
- std::string best_class;
- float best_score = -1;
- if (!span.classification.empty()) {
- best_class = span.classification[0].first;
- best_score = span.classification[0].second;
- }
- return os << "Span(" << span.span.first << ", " << span.span.second << ", "
- << best_class << ", " << best_score << ")";
-}
-
-} // namespace libtextclassifier
-
-#endif // LIBTEXTCLASSIFIER_SMARTSELECT_TEXT_CLASSIFICATION_MODEL_H_
diff --git a/smartselect/text-classification-model.proto b/smartselect/text-classification-model.proto
deleted file mode 100644
index 315e849..0000000
--- a/smartselect/text-classification-model.proto
+++ /dev/null
@@ -1,234 +0,0 @@
-// Copyright (C) 2017 The Android Open Source Project
-//
-// Licensed under the Apache License, Version 2.0 (the "License");
-// you may not use this file except in compliance with the License.
-// You may obtain a copy of the License at
-//
-// http://www.apache.org/licenses/LICENSE-2.0
-//
-// Unless required by applicable law or agreed to in writing, software
-// distributed under the License is distributed on an "AS IS" BASIS,
-// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-// See the License for the specific language governing permissions and
-// limitations under the License.
-
-// Text classification model configuration.
-
-syntax = "proto2";
-option optimize_for = LITE_RUNTIME;
-
-import "external/libtextclassifier/common/embedding-network.proto";
-import "external/libtextclassifier/smartselect/tokenizer.proto";
-
-package libtextclassifier;
-
-// Generic options of a model, non-specific to selection or sharing.
-message ModelOptions {
- // If true, will use embeddings from a different model. This is mainly useful
- // for the Sharing model using the embeddings from the Selection model.
- optional bool use_shared_embeddings = 1;
-
- // Language of the model.
- optional string language = 2;
-
- // Version of the model.
- optional int32 version = 3;
-}
-
-message SelectionModelOptions {
- // A list of Unicode codepoints to strip from predicted selections.
- repeated int32 deprecated_punctuation_to_strip = 1;
-
- // Enforce symmetrical selections.
- optional bool enforce_symmetry = 3;
-
- // Number of inferences made around the click position (to one side), for
- // enforcing symmetry.
- optional int32 symmetry_context_size = 4;
-
- // If true, before the selection is returned, the unpaired brackets contained
- // in the predicted selection are stripped from the both selection ends.
- // The bracket codepoints are defined in the Unicode standard:
- // http://www.unicode.org/Public/UNIDATA/BidiBrackets.txt
- optional bool strip_unpaired_brackets = 5 [default = true];
-
- reserved 2;
-}
-
-message SharingModelOptions {
- // If true, will always return "url" when the url hint is passed in.
- optional bool always_accept_url_hint = 1;
-
- // If true, will always return "email" when the e-mail hint is passed in.
- optional bool always_accept_email_hint = 2;
-
- // Limits for phone numbers.
- optional int32 phone_min_num_digits = 3 [default = 7];
- optional int32 phone_max_num_digits = 4 [default = 15];
-
- // List of regular expression matchers to check.
- message RegexPattern {
- // The name of the collection of a match.
- optional string collection_name = 1;
-
- // The pattern to check.
- optional string pattern = 2;
- }
- repeated RegexPattern regex_pattern = 5;
-}
-
-// Next ID: 41
-message FeatureProcessorOptions {
- // Number of buckets used for hashing charactergrams.
- optional int32 num_buckets = 1 [default = -1];
-
- // Context size defines the number of words to the left and to the right of
- // the selected word to be used as context. For example, if context size is
- // N, then we take N words to the left and N words to the right of the
- // selected word as its context.
- optional int32 context_size = 2 [default = -1];
-
- // Maximum number of words of the context to select in total.
- optional int32 max_selection_span = 3 [default = -1];
-
- // Orders of charactergrams to extract. E.g., 2 means character bigrams, 3
- // character trigrams etc.
- repeated int32 chargram_orders = 4;
-
- // Maximum length of a word, in codepoints.
- optional int32 max_word_length = 21 [default = 20];
-
- // If true, will use the unicode-aware functionality for extracting features.
- optional bool unicode_aware_features = 19 [default = false];
-
- // Whether to extract the token case feature.
- optional bool extract_case_feature = 5 [default = false];
-
- // Whether to extract the selection mask feature.
- optional bool extract_selection_mask_feature = 6 [default = false];
-
- // List of regexps to run over each token. For each regexp, if there is a
- // match, a dense feature of 1.0 is emitted. Otherwise -1.0 is used.
- repeated string regexp_feature = 22;
-
- // Whether to remap all digits to a single number.
- optional bool remap_digits = 20 [default = false];
-
- // Whether to lower-case each token before generating hashgrams.
- optional bool lowercase_tokens = 33;
-
- // If true, the selection classifier output will contain only the selections
- // that are feasible (e.g., those that are shorter than max_selection_span),
- // if false, the output will be a complete cross-product of possible
- // selections to the left and posible selections to the right, including the
- // infeasible ones.
- // NOTE: Exists mainly for compatibility with older models that were trained
- // with the non-reduced output space.
- optional bool selection_reduced_output_space = 8 [default = true];
-
- // Collection names.
- repeated string collections = 9;
-
- // An index of collection in collections to be used if a collection name can't
- // be mapped to an id.
- optional int32 default_collection = 10 [default = -1];
-
- // If true, will split the input by lines, and only use the line that contains
- // the clicked token.
- optional bool only_use_line_with_click = 13 [default = false];
-
- // If true, will split tokens that contain the selection boundary, at the
- // position of the boundary.
- // E.g. "foo{bar}@google.com" -> "foo", "bar", "@google.com"
- optional bool split_tokens_on_selection_boundaries = 14 [default = false];
-
- // Codepoint ranges that determine how different codepoints are tokenized.
- // The ranges must not overlap.
- repeated TokenizationCodepointRange tokenization_codepoint_config = 15;
-
- // Method for selecting the center token.
- enum CenterTokenSelectionMethod {
- DEFAULT_CENTER_TOKEN_METHOD = 0; // Invalid option.
-
- // Use click indices to determine the center token.
- CENTER_TOKEN_FROM_CLICK = 1;
-
- // Use selection indices to get a token range, and select the middle of it
- // as the center token.
- CENTER_TOKEN_MIDDLE_OF_SELECTION = 2;
- }
- optional CenterTokenSelectionMethod center_token_selection_method = 16;
-
- // If true, span boundaries will be snapped to containing tokens and not
- // required to exactly match token boundaries.
- optional bool snap_label_span_boundaries_to_containing_tokens = 18;
-
- // Range of codepoints start - end, where end is exclusive.
- message CodepointRange {
- optional int32 start = 1;
- optional int32 end = 2;
- }
-
- // A set of codepoint ranges supported by the model.
- repeated CodepointRange supported_codepoint_ranges = 23;
-
- // A set of codepoint ranges to use in the mixed tokenization mode to identify
- // stretches of tokens to re-tokenize using the internal tokenizer.
- repeated CodepointRange internal_tokenizer_codepoint_ranges = 34;
-
- // Minimum ratio of supported codepoints in the input context. If the ratio
- // is lower than this, the feature computation will fail.
- optional float min_supported_codepoint_ratio = 24 [default = 0.0];
-
- // Used for versioning the format of features the model expects.
- // - feature_version == 0:
- // For each token the features consist of:
- // - chargram embeddings
- // - dense features
- // Chargram embeddings for tokens are concatenated first together,
- // and at the end, the dense features for the tokens are concatenated
- // to it. So the resulting feature vector has two regions.
- optional int32 feature_version = 25 [default = 0];
-
- // Controls the type of tokenization the model will use for the input text.
- enum TokenizationType {
- INVALID_TOKENIZATION_TYPE = 0;
-
- // Use the internal tokenizer for tokenization.
- INTERNAL_TOKENIZER = 1;
-
- // Use ICU for tokenization.
- ICU = 2;
-
- // First apply ICU tokenization. Then identify stretches of tokens
- // consisting only of codepoints in internal_tokenizer_codepoint_ranges
- // and re-tokenize them using the internal tokenizer.
- MIXED = 3;
- }
- optional TokenizationType tokenization_type = 30
- [default = INTERNAL_TOKENIZER];
- optional bool icu_preserve_whitespace_tokens = 31 [default = false];
-
- // List of codepoints that will be stripped from beginning and end of
- // predicted spans.
- repeated int32 ignored_span_boundary_codepoints = 36;
-
- reserved 7, 11, 12, 26, 27, 28, 29, 32, 35, 39, 40;
-
- // List of allowed charactergrams. The extracted charactergrams are filtered
- // using this list, and charactergrams that are not present are interpreted as
- // out-of-vocabulary.
- // If no allowed_chargrams are specified, all charactergrams are allowed.
- // The field is typed as bytes type to allow non-UTF8 chargrams.
- repeated bytes allowed_chargrams = 38;
-};
-
-extend nlp_core.EmbeddingNetworkProto {
- optional ModelOptions model_options_in_embedding_network_proto = 150063045;
- optional FeatureProcessorOptions
- feature_processor_options_in_embedding_network_proto = 146230910;
- optional SelectionModelOptions
- selection_model_options_in_embedding_network_proto = 148190899;
- optional SharingModelOptions
- sharing_model_options_in_embedding_network_proto = 151445439;
-}
diff --git a/smartselect/text-classification-model_test.cc b/smartselect/text-classification-model_test.cc
deleted file mode 100644
index 5550e53..0000000
--- a/smartselect/text-classification-model_test.cc
+++ /dev/null
@@ -1,440 +0,0 @@
-/*
- * Copyright (C) 2017 The Android Open Source Project
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#include "smartselect/text-classification-model.h"
-
-#include <fcntl.h>
-#include <stdio.h>
-#include <fstream>
-#include <iostream>
-#include <memory>
-#include <string>
-
-#include "gtest/gtest.h"
-
-namespace libtextclassifier {
-namespace {
-
-std::string ReadFile(const std::string& file_name) {
- std::ifstream file_stream(file_name);
- return std::string(std::istreambuf_iterator<char>(file_stream), {});
-}
-
-std::string GetModelPath() {
- return TEST_DATA_DIR "smartselection.model";
-}
-
-std::string GetURLRegexPath() {
- return TEST_DATA_DIR "regex_url.txt";
-}
-
-std::string GetEmailRegexPath() {
- return TEST_DATA_DIR "regex_email.txt";
-}
-
-TEST(TextClassificationModelTest, StripUnpairedBrackets) {
- // Stripping brackets strip brackets from length 1 bracket only selections.
- EXPECT_EQ(StripUnpairedBrackets("call me at ) today", {11, 12}),
- std::make_pair(12, 12));
- EXPECT_EQ(StripUnpairedBrackets("call me at ( today", {11, 12}),
- std::make_pair(12, 12));
-}
-
-TEST(TextClassificationModelTest, ReadModelOptions) {
- const std::string model_path = GetModelPath();
- int fd = open(model_path.c_str(), O_RDONLY);
- ModelOptions model_options;
- ASSERT_TRUE(ReadSelectionModelOptions(fd, &model_options));
- close(fd);
-
- EXPECT_EQ("en", model_options.language());
- EXPECT_GT(model_options.version(), 0);
-}
-
-TEST(TextClassificationModelTest, SuggestSelection) {
- const std::string model_path = GetModelPath();
- int fd = open(model_path.c_str(), O_RDONLY);
- std::unique_ptr<TextClassificationModel> model(
- new TextClassificationModel(fd));
- close(fd);
-
- EXPECT_EQ(model->SuggestSelection(
- "this afternoon Barack Obama gave a speech at", {15, 21}),
- std::make_pair(15, 27));
-
- // Try passing whole string.
- // If more than 1 token is specified, we should return back what entered.
- EXPECT_EQ(model->SuggestSelection("350 Third Street, Cambridge", {0, 27}),
- std::make_pair(0, 27));
-
- // Single letter.
- EXPECT_EQ(std::make_pair(0, 1), model->SuggestSelection("a", {0, 1}));
-
- // Single word.
- EXPECT_EQ(std::make_pair(0, 4), model->SuggestSelection("asdf", {0, 4}));
-
- EXPECT_EQ(model->SuggestSelection("call me at 857 225 3556 today", {11, 14}),
- std::make_pair(11, 23));
-
- // Unpaired bracket stripping.
- EXPECT_EQ(
- model->SuggestSelection("call me at (857) 225 3556 today", {11, 16}),
- std::make_pair(11, 25));
- EXPECT_EQ(model->SuggestSelection("call me at (857 225 3556 today", {11, 15}),
- std::make_pair(12, 24));
- EXPECT_EQ(model->SuggestSelection("call me at 857 225 3556) today", {11, 14}),
- std::make_pair(11, 23));
- EXPECT_EQ(
- model->SuggestSelection("call me at )857 225 3556( today", {11, 15}),
- std::make_pair(12, 24));
-
- // If the resulting selection would be empty, the original span is returned.
- EXPECT_EQ(model->SuggestSelection("call me at )( today", {11, 13}),
- std::make_pair(11, 13));
- EXPECT_EQ(model->SuggestSelection("call me at ( today", {11, 12}),
- std::make_pair(11, 12));
- EXPECT_EQ(model->SuggestSelection("call me at ) today", {11, 12}),
- std::make_pair(11, 12));
-}
-
-TEST(TextClassificationModelTest, SuggestSelectionsAreSymmetric) {
- const std::string model_path = GetModelPath();
- int fd = open(model_path.c_str(), O_RDONLY);
- std::unique_ptr<TextClassificationModel> model(
- new TextClassificationModel(fd));
- close(fd);
-
- EXPECT_EQ(std::make_pair(0, 27),
- model->SuggestSelection("350 Third Street, Cambridge", {0, 3}));
- EXPECT_EQ(std::make_pair(0, 27),
- model->SuggestSelection("350 Third Street, Cambridge", {4, 9}));
- EXPECT_EQ(std::make_pair(0, 27),
- model->SuggestSelection("350 Third Street, Cambridge", {10, 16}));
- EXPECT_EQ(std::make_pair(6, 33),
- model->SuggestSelection("a\nb\nc\n350 Third Street, Cambridge",
- {16, 22}));
-}
-
-TEST(TextClassificationModelTest, SuggestSelectionWithNewLine) {
- const std::string model_path = GetModelPath();
- int fd = open(model_path.c_str(), O_RDONLY);
- std::unique_ptr<TextClassificationModel> model(
- new TextClassificationModel(fd));
- close(fd);
-
- std::tuple<int, int> selection;
- selection = model->SuggestSelection("abc\nBarack Obama", {4, 10});
- EXPECT_EQ(4, std::get<0>(selection));
- EXPECT_EQ(16, std::get<1>(selection));
-
- selection = model->SuggestSelection("Barack Obama\nabc", {0, 6});
- EXPECT_EQ(0, std::get<0>(selection));
- EXPECT_EQ(12, std::get<1>(selection));
-}
-
-TEST(TextClassificationModelTest, SuggestSelectionWithPunctuation) {
- const std::string model_path = GetModelPath();
- int fd = open(model_path.c_str(), O_RDONLY);
- std::unique_ptr<TextClassificationModel> model(
- new TextClassificationModel(fd));
- close(fd);
-
- std::tuple<int, int> selection;
-
- // From the right.
- selection = model->SuggestSelection(
- "this afternoon Barack Obama, gave a speech at", {15, 21});
- EXPECT_EQ(15, std::get<0>(selection));
- EXPECT_EQ(27, std::get<1>(selection));
-
- // From the right multiple.
- selection = model->SuggestSelection(
- "this afternoon Barack Obama,.,.,, gave a speech at", {15, 21});
- EXPECT_EQ(15, std::get<0>(selection));
- EXPECT_EQ(27, std::get<1>(selection));
-
- // From the left multiple.
- selection = model->SuggestSelection(
- "this afternoon ,.,.,,Barack Obama gave a speech at", {21, 27});
- EXPECT_EQ(21, std::get<0>(selection));
- EXPECT_EQ(27, std::get<1>(selection));
-
- // From both sides.
- selection = model->SuggestSelection(
- "this afternoon !Barack Obama,- gave a speech at", {16, 22});
- EXPECT_EQ(16, std::get<0>(selection));
- EXPECT_EQ(28, std::get<1>(selection));
-}
-
-class TestingTextClassificationModel
- : public libtextclassifier::TextClassificationModel {
- public:
- explicit TestingTextClassificationModel(int fd)
- : libtextclassifier::TextClassificationModel(fd) {}
-
- using TextClassificationModel::InitializeSharingRegexPatterns;
-
- void DisableClassificationHints() {
- sharing_options_.set_always_accept_url_hint(false);
- sharing_options_.set_always_accept_email_hint(false);
- }
-};
-
-TEST(TextClassificationModelTest, SuggestSelectionNoCrashWithJunk) {
- const std::string model_path = GetModelPath();
- int fd = open(model_path.c_str(), O_RDONLY);
- std::unique_ptr<TextClassificationModel> ff_model(
- new TextClassificationModel(fd));
- close(fd);
-
- std::tuple<int, int> selection;
-
- // Try passing in bunch of invalid selections.
- selection = ff_model->SuggestSelection("", {0, 27});
- // If more than 1 token is specified, we should return back what entered.
- EXPECT_EQ(0, std::get<0>(selection));
- EXPECT_EQ(27, std::get<1>(selection));
-
- selection = ff_model->SuggestSelection("", {-10, 27});
- // If more than 1 token is specified, we should return back what entered.
- EXPECT_EQ(-10, std::get<0>(selection));
- EXPECT_EQ(27, std::get<1>(selection));
-
- selection = ff_model->SuggestSelection("Word 1 2 3 hello!", {0, 27});
- // If more than 1 token is specified, we should return back what entered.
- EXPECT_EQ(0, std::get<0>(selection));
- EXPECT_EQ(27, std::get<1>(selection));
-
- selection = ff_model->SuggestSelection("Word 1 2 3 hello!", {-30, 300});
- // If more than 1 token is specified, we should return back what entered.
- EXPECT_EQ(-30, std::get<0>(selection));
- EXPECT_EQ(300, std::get<1>(selection));
-
- selection = ff_model->SuggestSelection("Word 1 2 3 hello!", {-10, -1});
- // If more than 1 token is specified, we should return back what entered.
- EXPECT_EQ(-10, std::get<0>(selection));
- EXPECT_EQ(-1, std::get<1>(selection));
-
- selection = ff_model->SuggestSelection("Word 1 2 3 hello!", {100, 17});
- // If more than 1 token is specified, we should return back what entered.
- EXPECT_EQ(100, std::get<0>(selection));
- EXPECT_EQ(17, std::get<1>(selection));
-}
-
-namespace {
-
-std::string FindBestResult(std::vector<std::pair<std::string, float>> results) {
- if (results.empty()) {
- return "<INVALID RESULTS>";
- }
-
- std::sort(results.begin(), results.end(),
- [](const std::pair<std::string, float> a,
- const std::pair<std::string, float> b) {
- return a.second > b.second;
- });
- return results[0].first;
-}
-
-} // namespace
-
-TEST(TextClassificationModelTest, ClassifyText) {
- const std::string model_path = GetModelPath();
- int fd = open(model_path.c_str(), O_RDONLY);
- std::unique_ptr<TestingTextClassificationModel> model(
- new TestingTextClassificationModel(fd));
- close(fd);
-
- model->DisableClassificationHints();
- EXPECT_EQ("other",
- FindBestResult(model->ClassifyText(
- "this afternoon Barack Obama gave a speech at", {15, 27})));
- EXPECT_EQ("other",
- FindBestResult(model->ClassifyText("you@android.com", {0, 15})));
- EXPECT_EQ("other", FindBestResult(model->ClassifyText(
- "Contact me at you@android.com", {14, 29})));
- EXPECT_EQ("phone", FindBestResult(model->ClassifyText(
- "Call me at (800) 123-456 today", {11, 24})));
- EXPECT_EQ("other", FindBestResult(model->ClassifyText(
- "Visit www.google.com every today!", {6, 20})));
-
- // More lines.
- EXPECT_EQ("other",
- FindBestResult(model->ClassifyText(
- "this afternoon Barack Obama gave a speech at|Visit "
- "www.google.com every today!|Call me at (800) 123-456 today.",
- {15, 27})));
- EXPECT_EQ("other",
- FindBestResult(model->ClassifyText(
- "this afternoon Barack Obama gave a speech at|Visit "
- "www.google.com every today!|Call me at (800) 123-456 today.",
- {51, 65})));
- EXPECT_EQ("phone",
- FindBestResult(model->ClassifyText(
- "this afternoon Barack Obama gave a speech at|Visit "
- "www.google.com every today!|Call me at (800) 123-456 today.",
- {90, 103})));
-
- // Single word.
- EXPECT_EQ("other", FindBestResult(model->ClassifyText("obama", {0, 5})));
- EXPECT_EQ("other", FindBestResult(model->ClassifyText("asdf", {0, 4})));
- EXPECT_EQ("<INVALID RESULTS>",
- FindBestResult(model->ClassifyText("asdf", {0, 0})));
-
- // Junk.
- EXPECT_EQ("<INVALID RESULTS>",
- FindBestResult(model->ClassifyText("", {0, 0})));
- EXPECT_EQ("<INVALID RESULTS>", FindBestResult(model->ClassifyText(
- "a\n\n\n\nx x x\n\n\n\n\n\n", {1, 5})));
-}
-
-TEST(TextClassificationModelTest, ClassifyTextWithHints) {
- const std::string model_path = GetModelPath();
- int fd = open(model_path.c_str(), O_RDONLY);
- std::unique_ptr<TestingTextClassificationModel> model(
- new TestingTextClassificationModel(fd));
- close(fd);
-
- // When EMAIL hint is passed, the result should be email.
- EXPECT_EQ("email",
- FindBestResult(model->ClassifyText(
- "x", {0, 1}, TextClassificationModel::SELECTION_IS_EMAIL)));
- // When URL hint is passed, the result should be email.
- EXPECT_EQ("url",
- FindBestResult(model->ClassifyText(
- "x", {0, 1}, TextClassificationModel::SELECTION_IS_URL)));
- // When both hints are passed, the result should be url (as it's probably
- // better to let Chrome handle this case).
- EXPECT_EQ("url", FindBestResult(model->ClassifyText(
- "x", {0, 1},
- TextClassificationModel::SELECTION_IS_EMAIL |
- TextClassificationModel::SELECTION_IS_URL)));
-
- // With disabled hints, we should get the same prediction regardless of the
- // hint.
- model->DisableClassificationHints();
- EXPECT_EQ(model->ClassifyText("x", {0, 1}, 0),
- model->ClassifyText("x", {0, 1},
- TextClassificationModel::SELECTION_IS_EMAIL));
-
- EXPECT_EQ(model->ClassifyText("x", {0, 1}, 0),
- model->ClassifyText("x", {0, 1},
- TextClassificationModel::SELECTION_IS_URL));
-}
-
-TEST(TextClassificationModelTest, PhoneFiltering) {
- const std::string model_path = GetModelPath();
- int fd = open(model_path.c_str(), O_RDONLY);
- std::unique_ptr<TestingTextClassificationModel> model(
- new TestingTextClassificationModel(fd));
- close(fd);
-
- EXPECT_EQ("phone", FindBestResult(model->ClassifyText("phone: (123) 456 789",
- {7, 20}, 0)));
- EXPECT_EQ("phone", FindBestResult(model->ClassifyText(
- "phone: (123) 456 789,0001112", {7, 25}, 0)));
- EXPECT_EQ("other", FindBestResult(model->ClassifyText(
- "phone: (123) 456 789,0001112", {7, 28}, 0)));
-}
-
-TEST(TextClassificationModelTest, Annotate) {
- const std::string model_path = GetModelPath();
- int fd = open(model_path.c_str(), O_RDONLY);
- std::unique_ptr<TestingTextClassificationModel> model(
- new TestingTextClassificationModel(fd));
- close(fd);
-
- std::string test_string =
- "& saw Barak Obama today .. 350 Third Street, Cambridge\nand my phone "
- "number is 853 225-3556.";
- std::vector<TextClassificationModel::AnnotatedSpan> result =
- model->Annotate(test_string);
-
- std::vector<TextClassificationModel::AnnotatedSpan> expected;
- expected.emplace_back();
- expected.back().span = {0, 0};
- expected.emplace_back();
- expected.back().span = {2, 5};
- expected.back().classification.push_back({"other", 1.0});
- expected.emplace_back();
- expected.back().span = {6, 17};
- expected.back().classification.push_back({"other", 1.0});
- expected.emplace_back();
- expected.back().span = {18, 23};
- expected.back().classification.push_back({"other", 1.0});
- expected.emplace_back();
- expected.back().span = {24, 24};
- expected.emplace_back();
- expected.back().span = {27, 54};
- expected.back().classification.push_back({"address", 1.0});
- expected.emplace_back();
- expected.back().span = {55, 58};
- expected.back().classification.push_back({"other", 1.0});
- expected.emplace_back();
- expected.back().span = {59, 61};
- expected.back().classification.push_back({"other", 1.0});
- expected.emplace_back();
- expected.back().span = {62, 74};
- expected.back().classification.push_back({"other", 1.0});
- expected.emplace_back();
- expected.back().span = {75, 77};
- expected.back().classification.push_back({"other", 1.0});
- expected.emplace_back();
- expected.back().span = {78, 90};
- expected.back().classification.push_back({"phone", 1.0});
-
- EXPECT_EQ(result.size(), expected.size());
- for (int i = 0; i < expected.size(); ++i) {
- EXPECT_EQ(result[i].span, expected[i].span) << result[i];
- if (!expected[i].classification.empty()) {
- EXPECT_GT(result[i].classification.size(), 0);
- EXPECT_EQ(result[i].classification[0].first,
- expected[i].classification[0].first)
- << result[i];
- }
- }
-}
-
-TEST(TextClassificationModelTest, URLEmailRegex) {
- const std::string model_path = GetModelPath();
- int fd = open(model_path.c_str(), O_RDONLY);
- std::unique_ptr<TestingTextClassificationModel> model(
- new TestingTextClassificationModel(fd));
- close(fd);
-
- SharingModelOptions options;
- SharingModelOptions::RegexPattern* email_pattern =
- options.add_regex_pattern();
- email_pattern->set_collection_name("email");
- email_pattern->set_pattern(ReadFile(GetEmailRegexPath()));
- SharingModelOptions::RegexPattern* url_pattern = options.add_regex_pattern();
- url_pattern->set_collection_name("url");
- url_pattern->set_pattern(ReadFile(GetURLRegexPath()));
-
- // TODO(b/69538802): Modify directly the model image instead.
- model->InitializeSharingRegexPatterns(
- {options.regex_pattern().begin(), options.regex_pattern().end()});
-
- EXPECT_EQ("url", FindBestResult(model->ClassifyText(
- "Visit www.google.com every today!", {6, 20})));
- EXPECT_EQ("email", FindBestResult(model->ClassifyText(
- "My email: asdf@something.cz", {10, 27})));
- EXPECT_EQ("url", FindBestResult(model->ClassifyText(
- "Login: http://asdf@something.cz", {7, 31})));
-}
-
-} // namespace
-} // namespace libtextclassifier
diff --git a/smartselect/tokenizer.cc b/smartselect/tokenizer.cc
deleted file mode 100644
index 2489a61..0000000
--- a/smartselect/tokenizer.cc
+++ /dev/null
@@ -1,95 +0,0 @@
-/*
- * Copyright (C) 2017 The Android Open Source Project
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#include "smartselect/tokenizer.h"
-
-#include <algorithm>
-
-#include "util/strings/utf8.h"
-#include "util/utf8/unicodetext.h"
-
-namespace libtextclassifier {
-
-Tokenizer::Tokenizer(
- const std::vector<TokenizationCodepointRange>& codepoint_ranges)
- : codepoint_ranges_(codepoint_ranges) {
- std::sort(codepoint_ranges_.begin(), codepoint_ranges_.end(),
- [](const TokenizationCodepointRange& a,
- const TokenizationCodepointRange& b) {
- return a.start() < b.start();
- });
-}
-
-TokenizationCodepointRange::Role Tokenizer::FindTokenizationRole(
- int codepoint) const {
- auto it = std::lower_bound(
- codepoint_ranges_.begin(), codepoint_ranges_.end(), codepoint,
- [](const TokenizationCodepointRange& range, int codepoint) {
- // This function compares range with the codepoint for the purpose of
- // finding the first greater or equal range. Because of the use of
- // std::lower_bound it needs to return true when range < codepoint;
- // the first time it will return false the lower bound is found and
- // returned.
- //
- // It might seem weird that the condition is range.end <= codepoint
- // here but when codepoint == range.end it means it's actually just
- // outside of the range, thus the range is less than the codepoint.
- return range.end() <= codepoint;
- });
- if (it != codepoint_ranges_.end() && it->start() <= codepoint &&
- it->end() > codepoint) {
- return it->role();
- } else {
- return TokenizationCodepointRange::DEFAULT_ROLE;
- }
-}
-
-std::vector<Token> Tokenizer::Tokenize(const std::string& utf8_text) const {
- UnicodeText context_unicode = UTF8ToUnicodeText(utf8_text, /*do_copy=*/false);
-
- std::vector<Token> result;
- Token new_token("", 0, 0);
- int codepoint_index = 0;
- for (auto it = context_unicode.begin(); it != context_unicode.end();
- ++it, ++codepoint_index) {
- TokenizationCodepointRange::Role role = FindTokenizationRole(*it);
- if (role & TokenizationCodepointRange::SPLIT_BEFORE) {
- if (!new_token.value.empty()) {
- result.push_back(new_token);
- }
- new_token = Token("", codepoint_index, codepoint_index);
- }
- if (!(role & TokenizationCodepointRange::DISCARD_CODEPOINT)) {
- new_token.value += std::string(
- it.utf8_data(),
- it.utf8_data() + GetNumBytesForNonZeroUTF8Char(it.utf8_data()));
- ++new_token.end;
- }
- if (role & TokenizationCodepointRange::SPLIT_AFTER) {
- if (!new_token.value.empty()) {
- result.push_back(new_token);
- }
- new_token = Token("", codepoint_index + 1, codepoint_index + 1);
- }
- }
- if (!new_token.value.empty()) {
- result.push_back(new_token);
- }
-
- return result;
-}
-
-} // namespace libtextclassifier
diff --git a/smartselect/tokenizer.h b/smartselect/tokenizer.h
deleted file mode 100644
index 4eb78f9..0000000
--- a/smartselect/tokenizer.h
+++ /dev/null
@@ -1,52 +0,0 @@
-/*
- * Copyright (C) 2017 The Android Open Source Project
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#ifndef LIBTEXTCLASSIFIER_SMARTSELECT_TOKENIZER_H_
-#define LIBTEXTCLASSIFIER_SMARTSELECT_TOKENIZER_H_
-
-#include <string>
-#include <vector>
-
-#include "smartselect/tokenizer.pb.h"
-#include "smartselect/types.h"
-
-namespace libtextclassifier {
-
-// Tokenizer splits the input string into a sequence of tokens, according to the
-// configuration.
-class Tokenizer {
- public:
- explicit Tokenizer(
- const std::vector<TokenizationCodepointRange>& codepoint_ranges);
-
- // Tokenizes the input string using the selected tokenization method.
- std::vector<Token> Tokenize(const std::string& utf8_text) const;
-
- protected:
- // Finds the tokenization role for given codepoint.
- // If the character is not found returns DEFAULT_ROLE.
- // Internally uses binary search so should be O(log(# of codepoint_ranges)).
- TokenizationCodepointRange::Role FindTokenizationRole(int codepoint) const;
-
- private:
- // Codepoint ranges that determine how different codepoints are tokenized.
- // The ranges must not overlap.
- std::vector<TokenizationCodepointRange> codepoint_ranges_;
-};
-
-} // namespace libtextclassifier
-
-#endif // LIBTEXTCLASSIFIER_SMARTSELECT_TOKENIZER_H_
diff --git a/smartselect/tokenizer.proto b/smartselect/tokenizer.proto
deleted file mode 100644
index 8e78970..0000000
--- a/smartselect/tokenizer.proto
+++ /dev/null
@@ -1,48 +0,0 @@
-// Copyright (C) 2017 The Android Open Source Project
-//
-// Licensed under the Apache License, Version 2.0 (the "License");
-// you may not use this file except in compliance with the License.
-// You may obtain a copy of the License at
-//
-// http://www.apache.org/licenses/LICENSE-2.0
-//
-// Unless required by applicable law or agreed to in writing, software
-// distributed under the License is distributed on an "AS IS" BASIS,
-// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-// See the License for the specific language governing permissions and
-// limitations under the License.
-
-syntax = "proto2";
-option optimize_for = LITE_RUNTIME;
-
-package libtextclassifier;
-
-// Represents a codepoint range [start, end) with its role for tokenization.
-message TokenizationCodepointRange {
- optional int32 start = 1;
- optional int32 end = 2;
-
- // Role of the codepoints in the range.
- enum Role {
- // Concatenates the codepoint to the current run of codepoints.
- DEFAULT_ROLE = 0;
-
- // Splits a run of codepoints before the current codepoint.
- SPLIT_BEFORE = 0x1;
-
- // Splits a run of codepoints after the current codepoint.
- SPLIT_AFTER = 0x2;
-
- // Discards the codepoint.
- DISCARD_CODEPOINT = 0x4;
-
- // Common values:
- // Splits on the characters and discards them. Good e.g. for the space
- // character.
- WHITESPACE_SEPARATOR = 0x7;
- // Each codepoint will be a separate token. Good e.g. for Chinese
- // characters.
- TOKEN_SEPARATOR = 0x3;
- }
- optional Role role = 3;
-}
diff --git a/smartselect/tokenizer_test.cc b/smartselect/tokenizer_test.cc
deleted file mode 100644
index cdb90a9..0000000
--- a/smartselect/tokenizer_test.cc
+++ /dev/null
@@ -1,261 +0,0 @@
-/*
- * Copyright (C) 2017 The Android Open Source Project
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#include "smartselect/tokenizer.h"
-
-#include "gmock/gmock.h"
-#include "gtest/gtest.h"
-
-namespace libtextclassifier {
-namespace {
-
-using testing::ElementsAreArray;
-
-class TestingTokenizer : public Tokenizer {
- public:
- explicit TestingTokenizer(
- const std::vector<TokenizationCodepointRange>& codepoint_range_configs)
- : Tokenizer(codepoint_range_configs) {}
-
- TokenizationCodepointRange::Role TestFindTokenizationRole(int c) const {
- return FindTokenizationRole(c);
- }
-};
-
-TEST(TokenizerTest, FindTokenizationRole) {
- std::vector<TokenizationCodepointRange> configs;
- TokenizationCodepointRange* config;
-
- configs.emplace_back();
- config = &configs.back();
- config->set_start(0);
- config->set_end(10);
- config->set_role(TokenizationCodepointRange::TOKEN_SEPARATOR);
-
- configs.emplace_back();
- config = &configs.back();
- config->set_start(32);
- config->set_end(33);
- config->set_role(TokenizationCodepointRange::WHITESPACE_SEPARATOR);
-
- configs.emplace_back();
- config = &configs.back();
- config->set_start(1234);
- config->set_end(12345);
- config->set_role(TokenizationCodepointRange::TOKEN_SEPARATOR);
-
- TestingTokenizer tokenizer(configs);
-
- // Test hits to the first group.
- EXPECT_EQ(tokenizer.TestFindTokenizationRole(0),
- TokenizationCodepointRange::TOKEN_SEPARATOR);
- EXPECT_EQ(tokenizer.TestFindTokenizationRole(5),
- TokenizationCodepointRange::TOKEN_SEPARATOR);
- EXPECT_EQ(tokenizer.TestFindTokenizationRole(10),
- TokenizationCodepointRange::DEFAULT_ROLE);
-
- // Test a hit to the second group.
- EXPECT_EQ(tokenizer.TestFindTokenizationRole(31),
- TokenizationCodepointRange::DEFAULT_ROLE);
- EXPECT_EQ(tokenizer.TestFindTokenizationRole(32),
- TokenizationCodepointRange::WHITESPACE_SEPARATOR);
- EXPECT_EQ(tokenizer.TestFindTokenizationRole(33),
- TokenizationCodepointRange::DEFAULT_ROLE);
-
- // Test hits to the third group.
- EXPECT_EQ(tokenizer.TestFindTokenizationRole(1233),
- TokenizationCodepointRange::DEFAULT_ROLE);
- EXPECT_EQ(tokenizer.TestFindTokenizationRole(1234),
- TokenizationCodepointRange::TOKEN_SEPARATOR);
- EXPECT_EQ(tokenizer.TestFindTokenizationRole(12344),
- TokenizationCodepointRange::TOKEN_SEPARATOR);
- EXPECT_EQ(tokenizer.TestFindTokenizationRole(12345),
- TokenizationCodepointRange::DEFAULT_ROLE);
-
- // Test a hit outside.
- EXPECT_EQ(tokenizer.TestFindTokenizationRole(99),
- TokenizationCodepointRange::DEFAULT_ROLE);
-}
-
-TEST(TokenizerTest, TokenizeOnSpace) {
- std::vector<TokenizationCodepointRange> configs;
- TokenizationCodepointRange* config;
-
- configs.emplace_back();
- config = &configs.back();
- // Space character.
- config->set_start(32);
- config->set_end(33);
- config->set_role(TokenizationCodepointRange::WHITESPACE_SEPARATOR);
-
- TestingTokenizer tokenizer(configs);
- std::vector<Token> tokens = tokenizer.Tokenize("Hello world!");
-
- EXPECT_THAT(tokens,
- ElementsAreArray({Token("Hello", 0, 5), Token("world!", 6, 12)}));
-}
-
-TEST(TokenizerTest, TokenizeComplex) {
- std::vector<TokenizationCodepointRange> configs;
- TokenizationCodepointRange* config;
-
- // Source: http://www.unicode.org/Public/10.0.0/ucd/Blocks-10.0.0d1.txt
- // Latin - cyrilic.
- // 0000..007F; Basic Latin
- // 0080..00FF; Latin-1 Supplement
- // 0100..017F; Latin Extended-A
- // 0180..024F; Latin Extended-B
- // 0250..02AF; IPA Extensions
- // 02B0..02FF; Spacing Modifier Letters
- // 0300..036F; Combining Diacritical Marks
- // 0370..03FF; Greek and Coptic
- // 0400..04FF; Cyrillic
- // 0500..052F; Cyrillic Supplement
- // 0530..058F; Armenian
- // 0590..05FF; Hebrew
- // 0600..06FF; Arabic
- // 0700..074F; Syriac
- // 0750..077F; Arabic Supplement
- configs.emplace_back();
- config = &configs.back();
- config->set_start(0);
- config->set_end(32);
- config->set_role(TokenizationCodepointRange::DEFAULT_ROLE);
- configs.emplace_back();
- config = &configs.back();
- config->set_start(32);
- config->set_end(33);
- config->set_role(TokenizationCodepointRange::WHITESPACE_SEPARATOR);
- configs.emplace_back();
- config = &configs.back();
- config->set_start(33);
- config->set_end(0x77F + 1);
- config->set_role(TokenizationCodepointRange::DEFAULT_ROLE);
-
- // CJK
- // 2E80..2EFF; CJK Radicals Supplement
- // 3000..303F; CJK Symbols and Punctuation
- // 3040..309F; Hiragana
- // 30A0..30FF; Katakana
- // 3100..312F; Bopomofo
- // 3130..318F; Hangul Compatibility Jamo
- // 3190..319F; Kanbun
- // 31A0..31BF; Bopomofo Extended
- // 31C0..31EF; CJK Strokes
- // 31F0..31FF; Katakana Phonetic Extensions
- // 3200..32FF; Enclosed CJK Letters and Months
- // 3300..33FF; CJK Compatibility
- // 3400..4DBF; CJK Unified Ideographs Extension A
- // 4DC0..4DFF; Yijing Hexagram Symbols
- // 4E00..9FFF; CJK Unified Ideographs
- // A000..A48F; Yi Syllables
- // A490..A4CF; Yi Radicals
- // A4D0..A4FF; Lisu
- // A500..A63F; Vai
- // F900..FAFF; CJK Compatibility Ideographs
- // FE30..FE4F; CJK Compatibility Forms
- // 20000..2A6DF; CJK Unified Ideographs Extension B
- // 2A700..2B73F; CJK Unified Ideographs Extension C
- // 2B740..2B81F; CJK Unified Ideographs Extension D
- // 2B820..2CEAF; CJK Unified Ideographs Extension E
- // 2CEB0..2EBEF; CJK Unified Ideographs Extension F
- // 2F800..2FA1F; CJK Compatibility Ideographs Supplement
- configs.emplace_back();
- config = &configs.back();
- config->set_start(0x2E80);
- config->set_end(0x2EFF + 1);
- config->set_role(TokenizationCodepointRange::TOKEN_SEPARATOR);
- configs.emplace_back();
- config = &configs.back();
- config->set_start(0x3000);
- config->set_end(0xA63F + 1);
- config->set_role(TokenizationCodepointRange::TOKEN_SEPARATOR);
- configs.emplace_back();
- config = &configs.back();
- config->set_start(0xF900);
- config->set_end(0xFAFF + 1);
- config->set_role(TokenizationCodepointRange::TOKEN_SEPARATOR);
- configs.emplace_back();
- config = &configs.back();
- config->set_start(0xFE30);
- config->set_end(0xFE4F + 1);
- config->set_role(TokenizationCodepointRange::TOKEN_SEPARATOR);
- configs.emplace_back();
- config = &configs.back();
- config->set_start(0x20000);
- config->set_end(0x2A6DF + 1);
- config->set_role(TokenizationCodepointRange::TOKEN_SEPARATOR);
- configs.emplace_back();
- config = &configs.back();
- config->set_start(0x2A700);
- config->set_end(0x2B73F + 1);
- config->set_role(TokenizationCodepointRange::TOKEN_SEPARATOR);
- configs.emplace_back();
- config = &configs.back();
- config->set_start(0x2B740);
- config->set_end(0x2B81F + 1);
- config->set_role(TokenizationCodepointRange::TOKEN_SEPARATOR);
- configs.emplace_back();
- config = &configs.back();
- config->set_start(0x2B820);
- config->set_end(0x2CEAF + 1);
- config->set_role(TokenizationCodepointRange::TOKEN_SEPARATOR);
- configs.emplace_back();
- config = &configs.back();
- config->set_start(0x2CEB0);
- config->set_end(0x2EBEF + 1);
- config->set_role(TokenizationCodepointRange::TOKEN_SEPARATOR);
- configs.emplace_back();
- config = &configs.back();
- config->set_start(0x2F800);
- config->set_end(0x2FA1F + 1);
- config->set_role(TokenizationCodepointRange::TOKEN_SEPARATOR);
-
- // Thai.
- // 0E00..0E7F; Thai
- configs.emplace_back();
- config = &configs.back();
- config->set_start(0x0E00);
- config->set_end(0x0E7F + 1);
- config->set_role(TokenizationCodepointRange::TOKEN_SEPARATOR);
-
- Tokenizer tokenizer(configs);
- std::vector<Token> tokens;
-
- tokens = tokenizer.Tokenize(
- "問少目木輸走猶術権自京門録球変。細開括省用掲情結傍走愛明氷。");
- EXPECT_EQ(tokens.size(), 30);
-
- tokens = tokenizer.Tokenize("問少目 hello 木輸ยามきゃ");
- // clang-format off
- EXPECT_THAT(
- tokens,
- ElementsAreArray({Token("問", 0, 1),
- Token("少", 1, 2),
- Token("目", 2, 3),
- Token("hello", 4, 9),
- Token("木", 10, 11),
- Token("輸", 11, 12),
- Token("ย", 12, 13),
- Token("า", 13, 14),
- Token("ม", 14, 15),
- Token("き", 15, 16),
- Token("ゃ", 16, 17)}));
- // clang-format on
-}
-
-} // namespace
-} // namespace libtextclassifier
diff --git a/smartselect/types.h b/smartselect/types.h
deleted file mode 100644
index 443e3ac..0000000
--- a/smartselect/types.h
+++ /dev/null
@@ -1,80 +0,0 @@
-/*
- * Copyright (C) 2017 The Android Open Source Project
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#ifndef LIBTEXTCLASSIFIER_SMARTSELECT_TYPES_H_
-#define LIBTEXTCLASSIFIER_SMARTSELECT_TYPES_H_
-
-#include <ostream>
-#include <string>
-#include <utility>
-
-namespace libtextclassifier {
-
-constexpr int kInvalidIndex = -1;
-
-// Index for a 0-based array of tokens.
-using TokenIndex = int;
-
-// Index for a 0-based array of codepoints.
-using CodepointIndex = int;
-
-// Marks a span in a sequence of codepoints. The first element is the index of
-// the first codepoint of the span, and the second element is the index of the
-// codepoint one past the end of the span.
-using CodepointSpan = std::pair<CodepointIndex, CodepointIndex>;
-
-// Marks a span in a sequence of tokens. The first element is the index of the
-// first token in the span, and the second element is the index of the token one
-// past the end of the span.
-using TokenSpan = std::pair<TokenIndex, TokenIndex>;
-
-// Token holds a token, its position in the original string and whether it was
-// part of the input span.
-struct Token {
- std::string value;
- CodepointIndex start;
- CodepointIndex end;
-
- // Whether the token is a padding token.
- bool is_padding;
-
- // Default constructor constructs the padding-token.
- Token()
- : value(""), start(kInvalidIndex), end(kInvalidIndex), is_padding(true) {}
-
- Token(const std::string& arg_value, CodepointIndex arg_start,
- CodepointIndex arg_end)
- : value(arg_value), start(arg_start), end(arg_end), is_padding(false) {}
-
- bool operator==(const Token& other) const {
- return value == other.value && start == other.start && end == other.end &&
- is_padding == other.is_padding;
- }
-
- bool IsContainedInSpan(CodepointSpan span) const {
- return start >= span.first && end <= span.second;
- }
-};
-
-// Pretty-printing function for Token.
-inline std::ostream& operator<<(std::ostream& os, const Token& token) {
- return os << "Token(\"" << token.value << "\", " << token.start << ", "
- << token.end << ", is_padding=" << token.is_padding << ")";
-}
-
-} // namespace libtextclassifier
-
-#endif // LIBTEXTCLASSIFIER_SMARTSELECT_TYPES_H_
diff --git a/strip-unpaired-brackets.cc b/strip-unpaired-brackets.cc
new file mode 100644
index 0000000..ddf3322
--- /dev/null
+++ b/strip-unpaired-brackets.cc
@@ -0,0 +1,105 @@
+/*
+ * Copyright (C) 2017 The Android Open Source Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "strip-unpaired-brackets.h"
+
+#include <iterator>
+
+#include "util/base/logging.h"
+#include "util/utf8/unicodetext.h"
+
+namespace libtextclassifier2 {
+namespace {
+
+// Returns true if given codepoint is contained in the given span in context.
+bool IsCodepointInSpan(const char32 codepoint,
+ const UnicodeText& context_unicode,
+ const CodepointSpan span) {
+ auto begin_it = context_unicode.begin();
+ std::advance(begin_it, span.first);
+ auto end_it = context_unicode.begin();
+ std::advance(end_it, span.second);
+
+ return std::find(begin_it, end_it, codepoint) != end_it;
+}
+
+// Returns the first codepoint of the span.
+char32 FirstSpanCodepoint(const UnicodeText& context_unicode,
+ const CodepointSpan span) {
+ auto it = context_unicode.begin();
+ std::advance(it, span.first);
+ return *it;
+}
+
+// Returns the last codepoint of the span.
+char32 LastSpanCodepoint(const UnicodeText& context_unicode,
+ const CodepointSpan span) {
+ auto it = context_unicode.begin();
+ std::advance(it, span.second - 1);
+ return *it;
+}
+
+} // namespace
+
+CodepointSpan StripUnpairedBrackets(const std::string& context,
+ CodepointSpan span, const UniLib& unilib) {
+ const UnicodeText context_unicode =
+ UTF8ToUnicodeText(context, /*do_copy=*/false);
+ return StripUnpairedBrackets(context_unicode, span, unilib);
+}
+
+// If the first or the last codepoint of the given span is a bracket, the
+// bracket is stripped if the span does not contain its corresponding paired
+// version.
+CodepointSpan StripUnpairedBrackets(const UnicodeText& context_unicode,
+ CodepointSpan span, const UniLib& unilib) {
+ if (context_unicode.empty() || !ValidNonEmptySpan(span)) {
+ return span;
+ }
+
+ const char32 begin_char = FirstSpanCodepoint(context_unicode, span);
+ const char32 paired_begin_char = unilib.GetPairedBracket(begin_char);
+ if (paired_begin_char != begin_char) {
+ if (!unilib.IsOpeningBracket(begin_char) ||
+ !IsCodepointInSpan(paired_begin_char, context_unicode, span)) {
+ ++span.first;
+ }
+ }
+
+ if (span.first == span.second) {
+ return span;
+ }
+
+ const char32 end_char = LastSpanCodepoint(context_unicode, span);
+ const char32 paired_end_char = unilib.GetPairedBracket(end_char);
+ if (paired_end_char != end_char) {
+ if (!unilib.IsClosingBracket(end_char) ||
+ !IsCodepointInSpan(paired_end_char, context_unicode, span)) {
+ --span.second;
+ }
+ }
+
+ // Should not happen, but let's make sure.
+ if (span.first > span.second) {
+ TC_LOG(WARNING) << "Inverse indices result: " << span.first << ", "
+ << span.second;
+ span.second = span.first;
+ }
+
+ return span;
+}
+
+} // namespace libtextclassifier2
diff --git a/strip-unpaired-brackets.h b/strip-unpaired-brackets.h
new file mode 100644
index 0000000..4e82c3e
--- /dev/null
+++ b/strip-unpaired-brackets.h
@@ -0,0 +1,38 @@
+/*
+ * Copyright (C) 2017 The Android Open Source Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#ifndef LIBTEXTCLASSIFIER_STRIP_UNPAIRED_BRACKETS_H_
+#define LIBTEXTCLASSIFIER_STRIP_UNPAIRED_BRACKETS_H_
+
+#include <string>
+
+#include "types.h"
+#include "util/utf8/unilib.h"
+
+namespace libtextclassifier2 {
+// If the first or the last codepoint of the given span is a bracket, the
+// bracket is stripped if the span does not contain its corresponding paired
+// version.
+CodepointSpan StripUnpairedBrackets(const std::string& context,
+ CodepointSpan span, const UniLib& unilib);
+
+// Same as above but takes UnicodeText instance directly.
+CodepointSpan StripUnpairedBrackets(const UnicodeText& context_unicode,
+ CodepointSpan span, const UniLib& unilib);
+
+} // namespace libtextclassifier2
+
+#endif // LIBTEXTCLASSIFIER_STRIP_UNPAIRED_BRACKETS_H_
diff --git a/strip-unpaired-brackets_test.cc b/strip-unpaired-brackets_test.cc
new file mode 100644
index 0000000..5362500
--- /dev/null
+++ b/strip-unpaired-brackets_test.cc
@@ -0,0 +1,61 @@
+/*
+ * Copyright (C) 2017 The Android Open Source Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "strip-unpaired-brackets.h"
+
+#include "gtest/gtest.h"
+
+namespace libtextclassifier2 {
+namespace {
+
+TEST(StripUnpairedBracketsTest, StripUnpairedBrackets) {
+ CREATE_UNILIB_FOR_TESTING
+ // If the brackets match, nothing gets stripped.
+ EXPECT_EQ(StripUnpairedBrackets("call me (123) 456 today", {8, 17}, unilib),
+ std::make_pair(8, 17));
+ EXPECT_EQ(StripUnpairedBrackets("call me (123 456) today", {8, 17}, unilib),
+ std::make_pair(8, 17));
+
+ // If the brackets don't match, they get stripped.
+ EXPECT_EQ(StripUnpairedBrackets("call me (123 456 today", {8, 16}, unilib),
+ std::make_pair(9, 16));
+ EXPECT_EQ(StripUnpairedBrackets("call me )123 456 today", {8, 16}, unilib),
+ std::make_pair(9, 16));
+ EXPECT_EQ(StripUnpairedBrackets("call me 123 456) today", {8, 16}, unilib),
+ std::make_pair(8, 15));
+ EXPECT_EQ(StripUnpairedBrackets("call me 123 456( today", {8, 16}, unilib),
+ std::make_pair(8, 15));
+
+ // Strips brackets correctly from length-1 selections that consist of
+ // a bracket only.
+ EXPECT_EQ(StripUnpairedBrackets("call me at ) today", {11, 12}, unilib),
+ std::make_pair(12, 12));
+ EXPECT_EQ(StripUnpairedBrackets("call me at ( today", {11, 12}, unilib),
+ std::make_pair(12, 12));
+
+ // Handles invalid spans gracefully.
+ EXPECT_EQ(StripUnpairedBrackets("call me at today", {11, 11}, unilib),
+ std::make_pair(11, 11));
+ EXPECT_EQ(StripUnpairedBrackets("hello world", {0, 0}, unilib),
+ std::make_pair(0, 0));
+ EXPECT_EQ(StripUnpairedBrackets("hello world", {11, 11}, unilib),
+ std::make_pair(11, 11));
+ EXPECT_EQ(StripUnpairedBrackets("hello world", {-1, -1}, unilib),
+ std::make_pair(-1, -1));
+}
+
+} // namespace
+} // namespace libtextclassifier2
diff --git a/common/config.h b/tensor-view.cc
similarity index 67%
rename from common/config.h
rename to tensor-view.cc
index b883e95..4acadc5 100644
--- a/common/config.h
+++ b/tensor-view.cc
@@ -14,16 +14,18 @@
* limitations under the License.
*/
-#ifndef LIBTEXTCLASSIFIER_COMMON_CONFIG_H_
-#define LIBTEXTCLASSIFIER_COMMON_CONFIG_H_
+#include "tensor-view.h"
-#ifndef PORTABLE_SAFT_MOBILE
-#if defined(__ANDROID__) || defined(__APPLE__)
-#define PORTABLE_SAFT_MOBILE 1
-#else
-#define PORTABLE_SAFT_MOBILE 0
-#endif
+namespace libtextclassifier2 {
-#endif
+namespace internal {
+int NumberOfElements(const std::vector<int>& shape) {
+ int size = 1;
+ for (const int dim : shape) {
+ size *= dim;
+ }
+ return size;
+}
+} // namespace internal
-#endif // LIBTEXTCLASSIFIER_COMMON_CONFIG_H_
+} // namespace libtextclassifier2
diff --git a/tensor-view.h b/tensor-view.h
new file mode 100644
index 0000000..00ab08c
--- /dev/null
+++ b/tensor-view.h
@@ -0,0 +1,72 @@
+/*
+ * Copyright (C) 2017 The Android Open Source Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#ifndef LIBTEXTCLASSIFIER_TENSOR_VIEW_H_
+#define LIBTEXTCLASSIFIER_TENSOR_VIEW_H_
+
+#include <algorithm>
+#include <vector>
+
+namespace libtextclassifier2 {
+namespace internal {
+// Computes the number of elements in a tensor of given shape.
+int NumberOfElements(const std::vector<int>& shape);
+} // namespace internal
+
+// View of a tensor of given type.
+// NOTE: Does not own the underlying memory, so the contract about its validity
+// needs to be specified on the interface that returns it.
+template <typename T>
+class TensorView {
+ public:
+ TensorView(const T* data, const std::vector<int>& shape)
+ : data_(data), shape_(shape), size_(internal::NumberOfElements(shape)) {}
+
+ static TensorView Invalid() {
+ static std::vector<int>& invalid_shape =
+ *[]() { return new std::vector<int>(0); }();
+ return TensorView(nullptr, invalid_shape);
+ }
+
+ bool is_valid() const { return data_ != nullptr; }
+
+ const std::vector<int>& shape() const { return shape_; }
+
+ int dim(int i) const { return shape_[i]; }
+
+ int dims() const { return shape_.size(); }
+
+ const T* data() const { return data_; }
+
+ int size() const { return size_; }
+
+ bool copy_to(T* dest, int dest_size) const {
+ if (dest_size < size_) {
+ return false;
+ }
+ std::copy(data_, data_ + size_, dest);
+ return true;
+ }
+
+ private:
+ const T* data_ = nullptr;
+ const std::vector<int> shape_;
+ const int size_;
+};
+
+} // namespace libtextclassifier2
+
+#endif // LIBTEXTCLASSIFIER_TENSOR_VIEW_H_
diff --git a/tensor-view_test.cc b/tensor-view_test.cc
new file mode 100644
index 0000000..d50fac7
--- /dev/null
+++ b/tensor-view_test.cc
@@ -0,0 +1,52 @@
+/*
+ * Copyright (C) 2017 The Android Open Source Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "tensor-view.h"
+
+#include "gmock/gmock.h"
+#include "gtest/gtest.h"
+
+namespace libtextclassifier2 {
+namespace {
+
+TEST(TensorViewTest, TestSize) {
+ std::vector<float> data{0.1, 0.2, 0.3, 0.4, 0.5, 0.6};
+ const TensorView<float> tensor(data.data(), {3, 1, 2});
+ EXPECT_TRUE(tensor.is_valid());
+ EXPECT_EQ(tensor.shape(), (std::vector<int>{3, 1, 2}));
+ EXPECT_EQ(tensor.data(), data.data());
+ EXPECT_EQ(tensor.size(), 6);
+ EXPECT_EQ(tensor.dims(), 3);
+ EXPECT_EQ(tensor.dim(0), 3);
+ EXPECT_EQ(tensor.dim(1), 1);
+ EXPECT_EQ(tensor.dim(2), 2);
+ std::vector<float> output_data(6);
+ EXPECT_TRUE(tensor.copy_to(output_data.data(), output_data.size()));
+ EXPECT_EQ(data, output_data);
+
+ // Should not copy when the output is small.
+ std::vector<float> small_output_data{-1, -1, -1};
+ EXPECT_FALSE(
+ tensor.copy_to(small_output_data.data(), small_output_data.size()));
+ // The output buffer should not be changed.
+ EXPECT_EQ(small_output_data, (std::vector<float>{-1, -1, -1}));
+
+ const TensorView<float> invalid_tensor = TensorView<float>::Invalid();
+ EXPECT_FALSE(invalid_tensor.is_valid());
+}
+
+} // namespace
+} // namespace libtextclassifier2
diff --git a/test_data/test_model.fb b/test_data/test_model.fb
new file mode 100644
index 0000000..c651bdb
--- /dev/null
+++ b/test_data/test_model.fb
Binary files differ
diff --git a/test_data/test_model_cc.fb b/test_data/test_model_cc.fb
new file mode 100644
index 0000000..53af6bf
--- /dev/null
+++ b/test_data/test_model_cc.fb
Binary files differ
diff --git a/test_data/wrong_embeddings.fb b/test_data/wrong_embeddings.fb
new file mode 100644
index 0000000..e1aa3ea
--- /dev/null
+++ b/test_data/wrong_embeddings.fb
Binary files differ
diff --git a/tests/testdata/langid.model b/tests/testdata/langid.model
deleted file mode 100644
index 6b68223..0000000
--- a/tests/testdata/langid.model
+++ /dev/null
Binary files differ
diff --git a/tests/testdata/smartselection.model b/tests/testdata/smartselection.model
deleted file mode 100644
index 645303d..0000000
--- a/tests/testdata/smartselection.model
+++ /dev/null
Binary files differ
diff --git a/text-classifier.cc b/text-classifier.cc
new file mode 100644
index 0000000..e20813a
--- /dev/null
+++ b/text-classifier.cc
@@ -0,0 +1,1576 @@
+/*
+ * Copyright (C) 2017 The Android Open Source Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "text-classifier.h"
+
+#include <algorithm>
+#include <cctype>
+#include <cmath>
+#include <iterator>
+#include <numeric>
+
+#include "util/base/logging.h"
+#include "util/math/softmax.h"
+#include "util/utf8/unicodetext.h"
+
+namespace libtextclassifier2 {
+const std::string& TextClassifier::kOtherCollection =
+ *[]() { return new std::string("other"); }();
+const std::string& TextClassifier::kPhoneCollection =
+ *[]() { return new std::string("phone"); }();
+const std::string& TextClassifier::kAddressCollection =
+ *[]() { return new std::string("address"); }();
+const std::string& TextClassifier::kDateCollection =
+ *[]() { return new std::string("date"); }();
+
+namespace {
+const Model* LoadAndVerifyModel(const void* addr, int size) {
+ const Model* model = GetModel(addr);
+
+ flatbuffers::Verifier verifier(reinterpret_cast<const uint8_t*>(addr), size);
+ if (model->Verify(verifier)) {
+ return model;
+ } else {
+ return nullptr;
+ }
+}
+} // namespace
+
+tflite::Interpreter* InterpreterManager::SelectionInterpreter() {
+ if (!selection_interpreter_) {
+ TC_CHECK(selection_executor_);
+ selection_interpreter_ = selection_executor_->CreateInterpreter();
+ if (!selection_interpreter_) {
+ TC_LOG(ERROR) << "Could not build TFLite interpreter.";
+ }
+ }
+ return selection_interpreter_.get();
+}
+
+tflite::Interpreter* InterpreterManager::ClassificationInterpreter() {
+ if (!classification_interpreter_) {
+ TC_CHECK(classification_executor_);
+ classification_interpreter_ = classification_executor_->CreateInterpreter();
+ if (!classification_interpreter_) {
+ TC_LOG(ERROR) << "Could not build TFLite interpreter.";
+ }
+ }
+ return classification_interpreter_.get();
+}
+
+std::unique_ptr<TextClassifier> TextClassifier::FromUnownedBuffer(
+ const char* buffer, int size, const UniLib* unilib) {
+ const Model* model = LoadAndVerifyModel(buffer, size);
+ if (model == nullptr) {
+ return nullptr;
+ }
+
+ auto classifier =
+ std::unique_ptr<TextClassifier>(new TextClassifier(model, unilib));
+ if (!classifier->IsInitialized()) {
+ return nullptr;
+ }
+
+ return classifier;
+}
+
+std::unique_ptr<TextClassifier> TextClassifier::FromScopedMmap(
+ std::unique_ptr<ScopedMmap>* mmap, const UniLib* unilib) {
+ if (!(*mmap)->handle().ok()) {
+ TC_VLOG(1) << "Mmap failed.";
+ return nullptr;
+ }
+
+ const Model* model = LoadAndVerifyModel((*mmap)->handle().start(),
+ (*mmap)->handle().num_bytes());
+ if (!model) {
+ TC_LOG(ERROR) << "Model verification failed.";
+ return nullptr;
+ }
+
+ auto classifier =
+ std::unique_ptr<TextClassifier>(new TextClassifier(mmap, model, unilib));
+ if (!classifier->IsInitialized()) {
+ return nullptr;
+ }
+
+ return classifier;
+}
+
+std::unique_ptr<TextClassifier> TextClassifier::FromFileDescriptor(
+ int fd, int offset, int size, const UniLib* unilib) {
+ std::unique_ptr<ScopedMmap> mmap(new ScopedMmap(fd, offset, size));
+ return FromScopedMmap(&mmap, unilib);
+}
+
+std::unique_ptr<TextClassifier> TextClassifier::FromFileDescriptor(
+ int fd, const UniLib* unilib) {
+ std::unique_ptr<ScopedMmap> mmap(new ScopedMmap(fd));
+ return FromScopedMmap(&mmap, unilib);
+}
+
+std::unique_ptr<TextClassifier> TextClassifier::FromPath(
+ const std::string& path, const UniLib* unilib) {
+ std::unique_ptr<ScopedMmap> mmap(new ScopedMmap(path));
+ return FromScopedMmap(&mmap, unilib);
+}
+
+void TextClassifier::ValidateAndInitialize() {
+ initialized_ = false;
+
+ if (model_ == nullptr) {
+ TC_LOG(ERROR) << "No model specified.";
+ return;
+ }
+
+ const bool model_enabled_for_annotation =
+ (model_->triggering_options() != nullptr &&
+ (model_->triggering_options()->enabled_modes() & ModeFlag_ANNOTATION));
+ const bool model_enabled_for_classification =
+ (model_->triggering_options() != nullptr &&
+ (model_->triggering_options()->enabled_modes() &
+ ModeFlag_CLASSIFICATION));
+ const bool model_enabled_for_selection =
+ (model_->triggering_options() != nullptr &&
+ (model_->triggering_options()->enabled_modes() & ModeFlag_SELECTION));
+
+ // Annotation requires the selection model.
+ if (model_enabled_for_annotation || model_enabled_for_selection) {
+ if (!model_->selection_options()) {
+ TC_LOG(ERROR) << "No selection options.";
+ return;
+ }
+ if (!model_->selection_feature_options()) {
+ TC_LOG(ERROR) << "No selection feature options.";
+ return;
+ }
+ if (!model_->selection_feature_options()->bounds_sensitive_features()) {
+ TC_LOG(ERROR) << "No selection bounds sensitive feature options.";
+ return;
+ }
+ if (!model_->selection_model()) {
+ TC_LOG(ERROR) << "No selection model.";
+ return;
+ }
+ selection_executor_ = ModelExecutor::Instance(model_->selection_model());
+ if (!selection_executor_) {
+ TC_LOG(ERROR) << "Could not initialize selection executor.";
+ return;
+ }
+ selection_feature_processor_.reset(
+ new FeatureProcessor(model_->selection_feature_options(), unilib_));
+ }
+
+ // Annotation requires the classification model for conflict resolution and
+ // scoring.
+ // Selection requires the classification model for conflict resolution.
+ if (model_enabled_for_annotation || model_enabled_for_classification ||
+ model_enabled_for_selection) {
+ if (!model_->classification_options()) {
+ TC_LOG(ERROR) << "No classification options.";
+ return;
+ }
+
+ if (!model_->classification_feature_options()) {
+ TC_LOG(ERROR) << "No classification feature options.";
+ return;
+ }
+
+ if (!model_->classification_feature_options()
+ ->bounds_sensitive_features()) {
+ TC_LOG(ERROR) << "No classification bounds sensitive feature options.";
+ return;
+ }
+ if (!model_->classification_model()) {
+ TC_LOG(ERROR) << "No clf model.";
+ return;
+ }
+
+ classification_executor_ =
+ ModelExecutor::Instance(model_->classification_model());
+ if (!classification_executor_) {
+ TC_LOG(ERROR) << "Could not initialize classification executor.";
+ return;
+ }
+
+ classification_feature_processor_.reset(new FeatureProcessor(
+ model_->classification_feature_options(), unilib_));
+ }
+
+ // The embeddings need to be specified if the model is to be used for
+ // classification or selection.
+ if (model_enabled_for_annotation || model_enabled_for_classification ||
+ model_enabled_for_selection) {
+ if (!model_->embedding_model()) {
+ TC_LOG(ERROR) << "No embedding model.";
+ return;
+ }
+
+ // Check that the embedding size of the selection and classification model
+ // matches, as they are using the same embeddings.
+ if (model_enabled_for_selection &&
+ (model_->selection_feature_options()->embedding_size() !=
+ model_->classification_feature_options()->embedding_size() ||
+ model_->selection_feature_options()->embedding_quantization_bits() !=
+ model_->classification_feature_options()
+ ->embedding_quantization_bits())) {
+ TC_LOG(ERROR) << "Mismatching embedding size/quantization.";
+ return;
+ }
+
+ embedding_executor_ = TFLiteEmbeddingExecutor::Instance(
+ model_->embedding_model(),
+ model_->classification_feature_options()->embedding_size(),
+ model_->classification_feature_options()
+ ->embedding_quantization_bits());
+ if (!embedding_executor_) {
+ TC_LOG(ERROR) << "Could not initialize embedding executor.";
+ return;
+ }
+ }
+
+ std::unique_ptr<ZlibDecompressor> decompressor = ZlibDecompressor::Instance();
+ if (model_->regex_model()) {
+ if (!InitializeRegexModel(decompressor.get())) {
+ TC_LOG(ERROR) << "Could not initialize regex model.";
+ return;
+ }
+ }
+
+ if (model_->datetime_model()) {
+ datetime_parser_ = DatetimeParser::Instance(model_->datetime_model(),
+ *unilib_, decompressor.get());
+ if (!datetime_parser_) {
+ TC_LOG(ERROR) << "Could not initialize datetime parser.";
+ return;
+ }
+ }
+
+ if (model_->output_options()) {
+ if (model_->output_options()->filtered_collections_annotation()) {
+ for (const auto collection :
+ *model_->output_options()->filtered_collections_annotation()) {
+ filtered_collections_annotation_.insert(collection->str());
+ }
+ }
+ if (model_->output_options()->filtered_collections_classification()) {
+ for (const auto collection :
+ *model_->output_options()->filtered_collections_classification()) {
+ filtered_collections_classification_.insert(collection->str());
+ }
+ }
+ if (model_->output_options()->filtered_collections_selection()) {
+ for (const auto collection :
+ *model_->output_options()->filtered_collections_selection()) {
+ filtered_collections_selection_.insert(collection->str());
+ }
+ }
+ }
+
+ initialized_ = true;
+}
+
+bool TextClassifier::InitializeRegexModel(ZlibDecompressor* decompressor) {
+ if (!model_->regex_model()->patterns()) {
+ return true;
+ }
+
+ // Initialize pattern recognizers.
+ int regex_pattern_id = 0;
+ for (const auto& regex_pattern : *model_->regex_model()->patterns()) {
+ std::unique_ptr<UniLib::RegexPattern> compiled_pattern =
+ UncompressMakeRegexPattern(*unilib_, regex_pattern->pattern(),
+ regex_pattern->compressed_pattern(),
+ decompressor);
+ if (!compiled_pattern) {
+ TC_LOG(INFO) << "Failed to load regex pattern";
+ return false;
+ }
+
+ if (regex_pattern->enabled_modes() & ModeFlag_ANNOTATION) {
+ annotation_regex_patterns_.push_back(regex_pattern_id);
+ }
+ if (regex_pattern->enabled_modes() & ModeFlag_CLASSIFICATION) {
+ classification_regex_patterns_.push_back(regex_pattern_id);
+ }
+ if (regex_pattern->enabled_modes() & ModeFlag_SELECTION) {
+ selection_regex_patterns_.push_back(regex_pattern_id);
+ }
+ regex_patterns_.push_back({regex_pattern->collection_name()->str(),
+ regex_pattern->target_classification_score(),
+ regex_pattern->priority_score(),
+ std::move(compiled_pattern)});
+ if (regex_pattern->use_approximate_matching()) {
+ regex_approximate_match_pattern_ids_.insert(regex_pattern_id);
+ }
+ ++regex_pattern_id;
+ }
+
+ return true;
+}
+
+namespace {
+
+int CountDigits(const std::string& str, CodepointSpan selection_indices) {
+ int count = 0;
+ int i = 0;
+ const UnicodeText unicode_str = UTF8ToUnicodeText(str, /*do_copy=*/false);
+ for (auto it = unicode_str.begin(); it != unicode_str.end(); ++it, ++i) {
+ if (i >= selection_indices.first && i < selection_indices.second &&
+ isdigit(*it)) {
+ ++count;
+ }
+ }
+ return count;
+}
+
+std::string ExtractSelection(const std::string& context,
+ CodepointSpan selection_indices) {
+ const UnicodeText context_unicode =
+ UTF8ToUnicodeText(context, /*do_copy=*/false);
+ auto selection_begin = context_unicode.begin();
+ std::advance(selection_begin, selection_indices.first);
+ auto selection_end = context_unicode.begin();
+ std::advance(selection_end, selection_indices.second);
+ return UnicodeText::UTF8Substring(selection_begin, selection_end);
+}
+} // namespace
+
+namespace internal {
+// Helper function, which if the initial 'span' contains only white-spaces,
+// moves the selection to a single-codepoint selection on a left or right side
+// of this space.
+CodepointSpan SnapLeftIfWhitespaceSelection(CodepointSpan span,
+ const UnicodeText& context_unicode,
+ const UniLib& unilib) {
+ TC_CHECK(ValidNonEmptySpan(span));
+
+ UnicodeText::const_iterator it;
+
+ // Check that the current selection is all whitespaces.
+ it = context_unicode.begin();
+ std::advance(it, span.first);
+ for (int i = 0; i < (span.second - span.first); ++i, ++it) {
+ if (!unilib.IsWhitespace(*it)) {
+ return span;
+ }
+ }
+
+ CodepointSpan result;
+
+ // Try moving left.
+ result = span;
+ it = context_unicode.begin();
+ std::advance(it, span.first);
+ while (it != context_unicode.begin() && unilib.IsWhitespace(*it)) {
+ --result.first;
+ --it;
+ }
+ result.second = result.first + 1;
+ if (!unilib.IsWhitespace(*it)) {
+ return result;
+ }
+
+ // If moving left didn't find a non-whitespace character, just return the
+ // original span.
+ return span;
+}
+} // namespace internal
+
+bool TextClassifier::FilteredForAnnotation(const AnnotatedSpan& span) const {
+ return !span.classification.empty() &&
+ filtered_collections_annotation_.find(
+ span.classification[0].collection) !=
+ filtered_collections_annotation_.end();
+}
+
+bool TextClassifier::FilteredForClassification(
+ const ClassificationResult& classification) const {
+ return filtered_collections_classification_.find(classification.collection) !=
+ filtered_collections_classification_.end();
+}
+
+bool TextClassifier::FilteredForSelection(const AnnotatedSpan& span) const {
+ return !span.classification.empty() &&
+ filtered_collections_selection_.find(
+ span.classification[0].collection) !=
+ filtered_collections_selection_.end();
+}
+
+CodepointSpan TextClassifier::SuggestSelection(
+ const std::string& context, CodepointSpan click_indices,
+ const SelectionOptions& options) const {
+ CodepointSpan original_click_indices = click_indices;
+ if (!initialized_) {
+ TC_LOG(ERROR) << "Not initialized";
+ return original_click_indices;
+ }
+ if (!(model_->enabled_modes() & ModeFlag_SELECTION)) {
+ 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) {
+ TC_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, neccessarily 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,
+ &interpreter_manager, &tokens, &candidates)) {
+ TC_LOG(ERROR) << "Model suggest selection failed.";
+ return original_click_indices;
+ }
+ if (!RegexChunk(context_unicode, selection_regex_patterns_, &candidates)) {
+ TC_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, &candidates)) {
+ TC_LOG(ERROR) << "Datetime suggest selection failed.";
+ 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, &interpreter_manager,
+ &candidate_indices)) {
+ TC_LOG(ERROR) << "Couldn't resolve conflicts.";
+ return original_click_indices;
+ }
+
+ 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, 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 TextClassifier::ResolveConflicts(
+ const std::vector<AnnotatedSpan>& candidates, const std::string& context,
+ const std::vector<Token>& cached_tokens,
+ 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, i,
+ first_non_overlapping, 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 {
+inline bool ClassifiedAsOther(
+ const std::vector<ClassificationResult>& classification) {
+ return !classification.empty() &&
+ classification[0].collection == TextClassifier::kOtherCollection;
+}
+
+float GetPriorityScore(
+ const std::vector<ClassificationResult>& classification) {
+ if (!ClassifiedAsOther(classification)) {
+ return classification[0].priority_score;
+ } else {
+ return -1.0;
+ }
+}
+} // namespace
+
+bool TextClassifier::ResolveConflict(
+ const std::string& context, const std::vector<Token>& cached_tokens,
+ const std::vector<AnnotatedSpan>& candidates, int start_index,
+ int end_index, 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, 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]; });
+
+ // Keeps the candidates sorted by their position in the text (their left span
+ // index) for fast retrieval down.
+ std::set<int, std::function<bool(int, int)>> chosen_indices_set(
+ [&candidates](int a, int b) {
+ return candidates[a].span.first < candidates[b].span.first;
+ });
+
+ // 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];
+ if (!DoesCandidateConflict(considered_candidate, candidates,
+ chosen_indices_set)) {
+ chosen_indices_set.insert(considered_candidate);
+ }
+ }
+
+ *chosen_indices =
+ std::vector<int>(chosen_indices_set.begin(), chosen_indices_set.end());
+
+ return true;
+}
+
+bool TextClassifier::ModelSuggestSelection(
+ const UnicodeText& context_unicode, CodepointSpan click_indices,
+ 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;
+ }
+
+ 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) {
+ TC_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)) {
+ TC_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)) {
+ TC_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 TextClassifier::ModelClassifyText(
+ const std::string& context, CodepointSpan selection_indices,
+ InterpreterManager* interpreter_manager,
+ FeatureProcessor::EmbeddingCache* embedding_cache,
+ std::vector<ClassificationResult>* classification_results) const {
+ if (model_->triggering_options() == nullptr ||
+ !(model_->triggering_options()->enabled_modes() &
+ ModeFlag_CLASSIFICATION)) {
+ return true;
+ }
+ return ModelClassifyText(context, {}, 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 TextClassifier::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};
+ }
+}
+
+bool TextClassifier::ModelClassifyText(
+ const std::string& context, const std::vector<Token>& cached_tokens,
+ CodepointSpan selection_indices, InterpreterManager* interpreter_manager,
+ FeatureProcessor::EmbeddingCache* embedding_cache,
+ std::vector<ClassificationResult>* classification_results) const {
+ std::vector<Token> tokens;
+ 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 = {{kOtherCollection, 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) {
+ TC_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) {
+ TC_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 = {{kOtherCollection, 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)) {
+ TC_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()) {
+ TC_LOG(ERROR) << "Couldn't compute logits.";
+ return false;
+ }
+
+ if (logits.dims() != 2 || logits.dim(0) != 1 ||
+ logits.dim(1) != classification_feature_processor_->NumCollections()) {
+ TC_LOG(ERROR) << "Mismatching output";
+ return false;
+ }
+
+ const std::vector<float> scores =
+ ComputeSoftmax(logits.data(), logits.dim(1));
+
+ classification_results->resize(scores.size());
+ for (int i = 0; i < scores.size(); i++) {
+ (*classification_results)[i] = {
+ classification_feature_processor_->LabelToCollection(i), scores[i]};
+ }
+ std::sort(classification_results->begin(), classification_results->end(),
+ [](const ClassificationResult& a, const ClassificationResult& b) {
+ return a.score > b.score;
+ });
+
+ // Phone class sanity check.
+ if (!classification_results->empty() &&
+ classification_results->begin()->collection == kPhoneCollection) {
+ 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 = {{kOtherCollection, 1.0}};
+ }
+ }
+
+ // Address class sanity check.
+ if (!classification_results->empty() &&
+ classification_results->begin()->collection == kAddressCollection) {
+ if (selection_num_tokens <
+ model_->classification_options()->address_min_num_tokens()) {
+ *classification_results = {{kOtherCollection, 1.0}};
+ }
+ }
+
+ return true;
+}
+
+bool TextClassifier::RegexClassifyText(
+ const std::string& context, CodepointSpan selection_indices,
+ ClassificationResult* classification_result) const {
+ const std::string selection_text =
+ ExtractSelection(context, selection_indices);
+ 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_approximate_match_pattern_ids_.find(pattern_id) !=
+ regex_approximate_match_pattern_ids_.end()) {
+ matches = matcher->ApproximatelyMatches(&status);
+ } else {
+ matches = matcher->Matches(&status);
+ }
+ if (status != UniLib::RegexMatcher::kNoError) {
+ return false;
+ }
+ if (matches) {
+ *classification_result = {regex_pattern.collection_name,
+ regex_pattern.target_classification_score,
+ regex_pattern.priority_score};
+ return true;
+ }
+ if (status != UniLib::RegexMatcher::kNoError) {
+ TC_LOG(ERROR) << "Cound't match regex: " << pattern_id;
+ }
+ }
+
+ return false;
+}
+
+bool TextClassifier::DatetimeClassifyText(
+ const std::string& context, CodepointSpan selection_indices,
+ const ClassificationOptions& options,
+ ClassificationResult* classification_result) const {
+ if (!datetime_parser_) {
+ return false;
+ }
+
+ const std::string selection_text =
+ ExtractSelection(context, selection_indices);
+
+ std::vector<DatetimeParseResultSpan> datetime_spans;
+ if (!datetime_parser_->Parse(selection_text, options.reference_time_ms_utc,
+ options.reference_timezone, options.locales,
+ ModeFlag_CLASSIFICATION,
+ /*anchor_start_end=*/true, &datetime_spans)) {
+ TC_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) {
+ *classification_result = {kDateCollection,
+ datetime_span.target_classification_score};
+ classification_result->datetime_parse_result = datetime_span.data;
+ return true;
+ }
+ }
+ return false;
+}
+
+std::vector<ClassificationResult> TextClassifier::ClassifyText(
+ const std::string& context, CodepointSpan selection_indices,
+ const ClassificationOptions& options) const {
+ if (!initialized_) {
+ TC_LOG(ERROR) << "Not initialized";
+ return {};
+ }
+
+ if (!(model_->enabled_modes() & ModeFlag_CLASSIFICATION)) {
+ return {};
+ }
+
+ if (!UTF8ToUnicodeText(context, /*do_copy=*/false).is_valid()) {
+ return {};
+ }
+
+ if (std::get<0>(selection_indices) >= std::get<1>(selection_indices)) {
+ TC_VLOG(1) << "Trying to run ClassifyText with invalid indices: "
+ << std::get<0>(selection_indices) << " "
+ << std::get<1>(selection_indices);
+ return {};
+ }
+
+ // Try the regular expression models.
+ ClassificationResult regex_result;
+ if (RegexClassifyText(context, selection_indices, ®ex_result)) {
+ if (!FilteredForClassification(regex_result)) {
+ return {regex_result};
+ } else {
+ return {{kOtherCollection, 1.0}};
+ }
+ }
+
+ // Try the date model.
+ ClassificationResult datetime_result;
+ if (DatetimeClassifyText(context, selection_indices, options,
+ &datetime_result)) {
+ if (!FilteredForClassification(datetime_result)) {
+ return {datetime_result};
+ } else {
+ return {{kOtherCollection, 1.0}};
+ }
+ }
+
+ // Fallback to the model.
+ std::vector<ClassificationResult> model_result;
+
+ InterpreterManager interpreter_manager(selection_executor_.get(),
+ classification_executor_.get());
+ if (ModelClassifyText(context, selection_indices, &interpreter_manager,
+ /*embedding_cache=*/nullptr, &model_result) &&
+ !model_result.empty()) {
+ if (!FilteredForClassification(model_result[0])) {
+ return model_result;
+ } else {
+ return {{kOtherCollection, 1.0}};
+ }
+ }
+
+ // No classifications.
+ return {};
+}
+
+bool TextClassifier::ModelAnnotate(const std::string& context,
+ 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;
+ }
+
+ 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);
+
+ FeatureProcessor::EmbeddingCache embedding_cache;
+ for (const UnicodeTextRange& line : lines) {
+ 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)) {
+ TC_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)) {
+ TC_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, codepoint_span,
+ interpreter_manager, &embedding_cache,
+ &classification)) {
+ TC_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* TextClassifier::SelectionFeatureProcessorForTests()
+ const {
+ return selection_feature_processor_.get();
+}
+
+const FeatureProcessor* TextClassifier::ClassificationFeatureProcessorForTests()
+ const {
+ return classification_feature_processor_.get();
+}
+
+const DatetimeParser* TextClassifier::DatetimeParserForTests() const {
+ return datetime_parser_.get();
+}
+
+std::vector<AnnotatedSpan> TextClassifier::Annotate(
+ const std::string& context, const AnnotationOptions& options) const {
+ std::vector<AnnotatedSpan> candidates;
+
+ if (!(model_->enabled_modes() & ModeFlag_ANNOTATION)) {
+ return {};
+ }
+
+ if (!UTF8ToUnicodeText(context, /*do_copy=*/false).is_valid()) {
+ return {};
+ }
+
+ InterpreterManager interpreter_manager(selection_executor_.get(),
+ classification_executor_.get());
+ // Annotate with the selection model.
+ std::vector<Token> tokens;
+ if (!ModelAnnotate(context, &interpreter_manager, &tokens, &candidates)) {
+ TC_LOG(ERROR) << "Couldn't run ModelAnnotate.";
+ return {};
+ }
+
+ // Annotate with the regular expression models.
+ if (!RegexChunk(UTF8ToUnicodeText(context, /*do_copy=*/false),
+ annotation_regex_patterns_, &candidates)) {
+ TC_LOG(ERROR) << "Couldn't run RegexChunk.";
+ return {};
+ }
+
+ // Annotate with the datetime model.
+ if (!DatetimeChunk(UTF8ToUnicodeText(context, /*do_copy=*/false),
+ options.reference_time_ms_utc, options.reference_timezone,
+ options.locales, ModeFlag_ANNOTATION, &candidates)) {
+ TC_LOG(ERROR) << "Couldn't run RegexChunk.";
+ 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, &interpreter_manager,
+ &candidate_indices)) {
+ TC_LOG(ERROR) << "Couldn't resolve conflicts.";
+ return {};
+ }
+
+ std::vector<AnnotatedSpan> result;
+ result.reserve(candidate_indices.size());
+ for (const int i : candidate_indices) {
+ if (!candidates[i].classification.empty() &&
+ !ClassifiedAsOther(candidates[i].classification) &&
+ !FilteredForAnnotation(candidates[i])) {
+ result.push_back(std::move(candidates[i]));
+ }
+ }
+
+ return result;
+}
+
+bool TextClassifier::RegexChunk(const UnicodeText& context_unicode,
+ const std::vector<int>& rules,
+ std::vector<AnnotatedSpan>* result) 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) {
+ TC_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) {
+ result->emplace_back();
+ // Selection/annotation regular expressions need to specify a capturing
+ // group specifying the selection.
+ result->back().span = {matcher->Start(1, &status),
+ matcher->End(1, &status)};
+ result->back().classification = {
+ {regex_pattern.collection_name,
+ regex_pattern.target_classification_score,
+ regex_pattern.priority_score}};
+ }
+ }
+ return true;
+}
+
+bool TextClassifier::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 TextClassifier::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()) {
+ TC_LOG(ERROR) << "Couldn't compute logits.";
+ return false;
+ }
+ if (logits.dims() != 2 || logits.dim(0) != batch_size ||
+ logits.dim(1) !=
+ selection_feature_processor_->GetSelectionLabelCount()) {
+ TC_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)) {
+ TC_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 TextClassifier::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()) {
+ TC_LOG(ERROR) << "Couldn't compute logits.";
+ return false;
+ }
+ if (logits.dims() != 2 || logits.dim(0) != batch_size ||
+ logits.dim(1) != 1) {
+ TC_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 TextClassifier::DatetimeChunk(const UnicodeText& context_unicode,
+ int64 reference_time_ms_utc,
+ const std::string& reference_timezone,
+ const std::string& locales, ModeFlag mode,
+ 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,
+ /*anchor_start_end=*/false, &datetime_spans)) {
+ return false;
+ }
+ for (const DatetimeParseResultSpan& datetime_span : datetime_spans) {
+ AnnotatedSpan annotated_span;
+ annotated_span.span = datetime_span.span;
+ annotated_span.classification = {{kDateCollection,
+ datetime_span.target_classification_score,
+ datetime_span.priority_score}};
+ annotated_span.classification[0].datetime_parse_result = datetime_span.data;
+
+ result->push_back(std::move(annotated_span));
+ }
+ return true;
+}
+
+const Model* ViewModel(const void* buffer, int size) {
+ if (!buffer) {
+ return nullptr;
+ }
+
+ return LoadAndVerifyModel(buffer, size);
+}
+
+} // namespace libtextclassifier2
diff --git a/text-classifier.h b/text-classifier.h
new file mode 100644
index 0000000..0692ecd
--- /dev/null
+++ b/text-classifier.h
@@ -0,0 +1,381 @@
+/*
+ * Copyright (C) 2017 The Android Open Source Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+// Inference code for the text classification model.
+
+#ifndef LIBTEXTCLASSIFIER_TEXT_CLASSIFIER_H_
+#define LIBTEXTCLASSIFIER_TEXT_CLASSIFIER_H_
+
+#include <memory>
+#include <set>
+#include <string>
+#include <vector>
+
+#include "datetime/parser.h"
+#include "feature-processor.h"
+#include "model-executor.h"
+#include "model_generated.h"
+#include "strip-unpaired-brackets.h"
+#include "types.h"
+#include "util/memory/mmap.h"
+#include "util/utf8/unilib.h"
+#include "zlib-utils.h"
+
+namespace libtextclassifier2 {
+
+struct SelectionOptions {
+ // Comma-separated list of locale specification for the input text (BCP 47
+ // tags).
+ std::string locales;
+
+ static SelectionOptions Default() { return SelectionOptions(); }
+};
+
+struct ClassificationOptions {
+ // For parsing relative datetimes, the reference now time against which the
+ // relative datetimes get resolved.
+ // UTC milliseconds since epoch.
+ int64 reference_time_ms_utc = 0;
+
+ // Timezone in which the input text was written (format as accepted by ICU).
+ std::string reference_timezone;
+
+ // Comma-separated list of locale specification for the input text (BCP 47
+ // tags).
+ std::string locales;
+
+ static ClassificationOptions Default() { return ClassificationOptions(); }
+};
+
+struct AnnotationOptions {
+ // For parsing relative datetimes, the reference now time against which the
+ // relative datetimes get resolved.
+ // UTC milliseconds since epoch.
+ int64 reference_time_ms_utc = 0;
+
+ // Timezone in which the input text was written (format as accepted by ICU).
+ std::string reference_timezone;
+
+ // Comma-separated list of locale specification for the input text (BCP 47
+ // tags).
+ std::string locales;
+
+ static AnnotationOptions Default() { return AnnotationOptions(); }
+};
+
+// Holds TFLite interpreters for selection and classification models.
+// NOTE: his class is not thread-safe, thus should NOT be re-used across
+// threads.
+class InterpreterManager {
+ public:
+ // The constructor can be called with nullptr for any of the executors, and is
+ // a defined behavior, as long as the corresponding *Interpreter() method is
+ // not called when the executor is null.
+ InterpreterManager(const ModelExecutor* selection_executor,
+ const ModelExecutor* classification_executor)
+ : selection_executor_(selection_executor),
+ classification_executor_(classification_executor) {}
+
+ // Gets or creates and caches an interpreter for the selection model.
+ tflite::Interpreter* SelectionInterpreter();
+
+ // Gets or creates and caches an interpreter for the classification model.
+ tflite::Interpreter* ClassificationInterpreter();
+
+ private:
+ const ModelExecutor* selection_executor_;
+ const ModelExecutor* classification_executor_;
+
+ std::unique_ptr<tflite::Interpreter> selection_interpreter_;
+ std::unique_ptr<tflite::Interpreter> classification_interpreter_;
+};
+
+// A text processing model that provides text classification, annotation,
+// selection suggestion for various types.
+// NOTE: This class is not thread-safe.
+class TextClassifier {
+ public:
+ static std::unique_ptr<TextClassifier> FromUnownedBuffer(
+ const char* buffer, int size, const UniLib* unilib = nullptr);
+ // Takes ownership of the mmap.
+ static std::unique_ptr<TextClassifier> FromScopedMmap(
+ std::unique_ptr<ScopedMmap>* mmap, const UniLib* unilib = nullptr);
+ static std::unique_ptr<TextClassifier> FromFileDescriptor(
+ int fd, int offset, int size, const UniLib* unilib = nullptr);
+ static std::unique_ptr<TextClassifier> FromFileDescriptor(
+ int fd, const UniLib* unilib = nullptr);
+ static std::unique_ptr<TextClassifier> FromPath(
+ const std::string& path, const UniLib* unilib = nullptr);
+
+ // Returns true if the model is ready for use.
+ bool IsInitialized() { return initialized_; }
+
+ // Runs inference for given a context and current selection (i.e. index
+ // of the first and one past last selected characters (utf8 codepoint
+ // offsets)). Returns the indices (utf8 codepoint offsets) of the selection
+ // beginning character and one past selection end character.
+ // Returns the original click_indices if an error occurs.
+ // NOTE: The selection indices are passed in and returned in terms of
+ // UTF8 codepoints (not bytes).
+ // Requires that the model is a smart selection model.
+ CodepointSpan SuggestSelection(
+ const std::string& context, CodepointSpan click_indices,
+ const SelectionOptions& options = SelectionOptions::Default()) const;
+
+ // Classifies the selected text given the context string.
+ // Returns an empty result if an error occurs.
+ std::vector<ClassificationResult> ClassifyText(
+ const std::string& context, CodepointSpan selection_indices,
+ const ClassificationOptions& options =
+ ClassificationOptions::Default()) const;
+
+ // Annotates given input text. The annotations are sorted by their position
+ // in the context string and exclude spans classified as 'other'.
+ std::vector<AnnotatedSpan> Annotate(
+ const std::string& context,
+ const AnnotationOptions& options = AnnotationOptions::Default()) const;
+
+ // Exposes the feature processor for tests and evaluations.
+ const FeatureProcessor* SelectionFeatureProcessorForTests() const;
+ const FeatureProcessor* ClassificationFeatureProcessorForTests() const;
+
+ // Exposes the date time parser for tests and evaluations.
+ const DatetimeParser* DatetimeParserForTests() const;
+
+ // String collection names for various classes.
+ static const std::string& kOtherCollection;
+ static const std::string& kPhoneCollection;
+ static const std::string& kAddressCollection;
+ static const std::string& kDateCollection;
+
+ protected:
+ struct ScoredChunk {
+ TokenSpan token_span;
+ float score;
+ };
+
+ // Constructs and initializes text classifier from given model.
+ // Takes ownership of 'mmap', and thus owns the buffer that backs 'model'.
+ TextClassifier(std::unique_ptr<ScopedMmap>* mmap, const Model* model,
+ const UniLib* unilib)
+ : model_(model),
+ mmap_(std::move(*mmap)),
+ owned_unilib_(nullptr),
+ unilib_(internal::MaybeCreateUnilib(unilib, &owned_unilib_)) {
+ ValidateAndInitialize();
+ }
+
+ // Constructs, validates and initializes text classifier from given model.
+ // Does not own the buffer that backs 'model'.
+ explicit TextClassifier(const Model* model, const UniLib* unilib)
+ : model_(model),
+ owned_unilib_(nullptr),
+ unilib_(internal::MaybeCreateUnilib(unilib, &owned_unilib_)) {
+ ValidateAndInitialize();
+ }
+
+ // Checks that model contains all required fields, and initializes internal
+ // datastructures.
+ void ValidateAndInitialize();
+
+ // Initializes regular expressions for the regex model.
+ bool InitializeRegexModel(ZlibDecompressor* decompressor);
+
+ // Resolves conflicts in the list of candidates by removing some overlapping
+ // ones. Returns indices of the surviving ones.
+ // NOTE: Assumes that the candidates are sorted according to their position in
+ // the span.
+ bool ResolveConflicts(const std::vector<AnnotatedSpan>& candidates,
+ const std::string& context,
+ const std::vector<Token>& cached_tokens,
+ InterpreterManager* interpreter_manager,
+ std::vector<int>* result) const;
+
+ // Resolves one conflict between candidates on indices 'start_index'
+ // (inclusive) and 'end_index' (exclusive). Assigns the winning candidate
+ // indices to 'chosen_indices'. Returns false if a problem arises.
+ bool ResolveConflict(const std::string& context,
+ const std::vector<Token>& cached_tokens,
+ const std::vector<AnnotatedSpan>& candidates,
+ int start_index, int end_index,
+ InterpreterManager* interpreter_manager,
+ std::vector<int>* chosen_indices) const;
+
+ // Gets selection candidates from the ML model.
+ // Provides the tokens produced during tokenization of the context string for
+ // reuse.
+ bool ModelSuggestSelection(const UnicodeText& context_unicode,
+ CodepointSpan click_indices,
+ InterpreterManager* interpreter_manager,
+ std::vector<Token>* tokens,
+ std::vector<AnnotatedSpan>* result) const;
+
+ // Classifies the selected text given the context string with the
+ // classification model.
+ // Returns true if no error occurred.
+ bool ModelClassifyText(
+ const std::string& context, const std::vector<Token>& cached_tokens,
+ CodepointSpan selection_indices, InterpreterManager* interpreter_manager,
+ FeatureProcessor::EmbeddingCache* embedding_cache,
+ std::vector<ClassificationResult>* classification_results) const;
+
+ bool ModelClassifyText(
+ const std::string& context, CodepointSpan selection_indices,
+ InterpreterManager* interpreter_manager,
+ FeatureProcessor::EmbeddingCache* embedding_cache,
+ std::vector<ClassificationResult>* classification_results) const;
+
+ // Returns a relative token span that represents how many tokens on the left
+ // from the selection and right from the selection are needed for the
+ // classifier input.
+ TokenSpan ClassifyTextUpperBoundNeededTokens() const;
+
+ // Classifies the selected text with the regular expressions models.
+ // Returns true if any regular expression matched and the result was set.
+ bool RegexClassifyText(const std::string& context,
+ CodepointSpan selection_indices,
+ ClassificationResult* classification_result) const;
+
+ // Classifies the selected text with the date time model.
+ // Returns true if there was a match and the result was set.
+ bool DatetimeClassifyText(const std::string& context,
+ CodepointSpan selection_indices,
+ const ClassificationOptions& options,
+ ClassificationResult* classification_result) const;
+
+ // Chunks given input text with the selection model and classifies the spans
+ // with the classification model.
+ // The annotations are sorted by their position in the context string and
+ // exclude spans classified as 'other'.
+ // Provides the tokens produced during tokenization of the context string for
+ // reuse.
+ bool ModelAnnotate(const std::string& context,
+ InterpreterManager* interpreter_manager,
+ std::vector<Token>* tokens,
+ std::vector<AnnotatedSpan>* result) const;
+
+ // Groups the tokens into chunks. A chunk is a token span that should be the
+ // suggested selection when any of its contained tokens is clicked. The chunks
+ // are non-overlapping and are sorted by their position in the context string.
+ // "num_tokens" is the total number of tokens available (as this method does
+ // not need the actual vector of tokens).
+ // "span_of_interest" is a span of all the tokens that could be clicked.
+ // The resulting chunks all have to overlap with it and they cover this span
+ // completely. The first and last chunk might extend beyond it.
+ // The chunks vector is cleared before filling.
+ bool ModelChunk(int num_tokens, const TokenSpan& span_of_interest,
+ tflite::Interpreter* selection_interpreter,
+ const CachedFeatures& cached_features,
+ std::vector<TokenSpan>* chunks) const;
+
+ // A helper method for ModelChunk(). It generates scored chunk candidates for
+ // a click context model.
+ // NOTE: The returned chunks can (and most likely do) overlap.
+ bool ModelClickContextScoreChunks(
+ int num_tokens, const TokenSpan& span_of_interest,
+ const CachedFeatures& cached_features,
+ tflite::Interpreter* selection_interpreter,
+ std::vector<ScoredChunk>* scored_chunks) const;
+
+ // A helper method for ModelChunk(). It generates scored chunk candidates for
+ // a bounds-sensitive model.
+ // NOTE: The returned chunks can (and most likely do) overlap.
+ bool 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;
+
+ // Produces chunks isolated by a set of regular expressions.
+ bool RegexChunk(const UnicodeText& context_unicode,
+ const std::vector<int>& rules,
+ std::vector<AnnotatedSpan>* result) const;
+
+ // Produces chunks from the datetime parser.
+ bool DatetimeChunk(const UnicodeText& context_unicode,
+ int64 reference_time_ms_utc,
+ const std::string& reference_timezone,
+ const std::string& locales, ModeFlag mode,
+ std::vector<AnnotatedSpan>* result) const;
+
+ // Returns whether a classification should be filtered.
+ bool FilteredForAnnotation(const AnnotatedSpan& span) const;
+ bool FilteredForClassification(
+ const ClassificationResult& classification) const;
+ bool FilteredForSelection(const AnnotatedSpan& span) const;
+
+ const Model* model_;
+
+ std::unique_ptr<const ModelExecutor> selection_executor_;
+ std::unique_ptr<const ModelExecutor> classification_executor_;
+ std::unique_ptr<const EmbeddingExecutor> embedding_executor_;
+
+ std::unique_ptr<const FeatureProcessor> selection_feature_processor_;
+ std::unique_ptr<const FeatureProcessor> classification_feature_processor_;
+
+ std::unique_ptr<const DatetimeParser> datetime_parser_;
+
+ private:
+ struct CompiledRegexPattern {
+ std::string collection_name;
+ float target_classification_score;
+ float priority_score;
+ std::unique_ptr<UniLib::RegexPattern> pattern;
+ };
+
+ std::unique_ptr<ScopedMmap> mmap_;
+ bool initialized_ = false;
+ bool enabled_for_annotation_ = false;
+ bool enabled_for_classification_ = false;
+ bool enabled_for_selection_ = false;
+ std::unordered_set<std::string> filtered_collections_annotation_;
+ std::unordered_set<std::string> filtered_collections_classification_;
+ std::unordered_set<std::string> filtered_collections_selection_;
+
+ std::vector<CompiledRegexPattern> regex_patterns_;
+ std::unordered_set<int> regex_approximate_match_pattern_ids_;
+
+ // Indices into regex_patterns_ for the different modes.
+ std::vector<int> annotation_regex_patterns_, classification_regex_patterns_,
+ selection_regex_patterns_;
+
+ std::unique_ptr<UniLib> owned_unilib_;
+ const UniLib* unilib_;
+};
+
+namespace internal {
+
+// Helper function, which if the initial 'span' contains only white-spaces,
+// moves the selection to a single-codepoint selection on the left side
+// of this block of white-space.
+CodepointSpan SnapLeftIfWhitespaceSelection(CodepointSpan span,
+ const UnicodeText& context_unicode,
+ const UniLib& unilib);
+
+// Copies tokens from 'cached_tokens' that are
+// 'tokens_around_selection_to_copy' (on the left, and right) tokens distant
+// from the tokens that correspond to 'selection_indices'.
+std::vector<Token> CopyCachedTokens(const std::vector<Token>& cached_tokens,
+ CodepointSpan selection_indices,
+ TokenSpan tokens_around_selection_to_copy);
+} // namespace internal
+
+// Interprets the buffer as a Model flatbuffer and returns it for reading.
+const Model* ViewModel(const void* buffer, int size);
+
+} // namespace libtextclassifier2
+
+#endif // LIBTEXTCLASSIFIER_TEXT_CLASSIFIER_H_
diff --git a/text-classifier_test.cc b/text-classifier_test.cc
new file mode 100644
index 0000000..c8ced76
--- /dev/null
+++ b/text-classifier_test.cc
@@ -0,0 +1,1291 @@
+/*
+ * Copyright (C) 2017 The Android Open Source Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "text-classifier.h"
+
+#include <fstream>
+#include <iostream>
+#include <memory>
+#include <string>
+
+#include "model_generated.h"
+#include "types-test-util.h"
+#include "gmock/gmock.h"
+#include "gtest/gtest.h"
+
+namespace libtextclassifier2 {
+namespace {
+
+using testing::ElementsAreArray;
+using testing::IsEmpty;
+using testing::Pair;
+using testing::Values;
+
+std::string FirstResult(const std::vector<ClassificationResult>& results) {
+ if (results.empty()) {
+ return "<INVALID RESULTS>";
+ }
+ return results[0].collection;
+}
+
+MATCHER_P3(IsAnnotatedSpan, start, end, best_class, "") {
+ return testing::Value(arg.span, Pair(start, end)) &&
+ testing::Value(FirstResult(arg.classification), best_class);
+}
+
+std::string ReadFile(const std::string& file_name) {
+ std::ifstream file_stream(file_name);
+ return std::string(std::istreambuf_iterator<char>(file_stream), {});
+}
+
+std::string GetModelPath() {
+ return LIBTEXTCLASSIFIER_TEST_DATA_DIR;
+}
+
+TEST(TextClassifierTest, EmbeddingExecutorLoadingFails) {
+ CREATE_UNILIB_FOR_TESTING;
+ std::unique_ptr<TextClassifier> classifier =
+ TextClassifier::FromPath(GetModelPath() + "wrong_embeddings.fb", &unilib);
+ EXPECT_FALSE(classifier);
+}
+
+class TextClassifierTest : public ::testing::TestWithParam<const char*> {};
+
+INSTANTIATE_TEST_CASE_P(ClickContext, TextClassifierTest,
+ Values("test_model_cc.fb"));
+INSTANTIATE_TEST_CASE_P(BoundsSensitive, TextClassifierTest,
+ Values("test_model.fb"));
+
+TEST_P(TextClassifierTest, ClassifyText) {
+ CREATE_UNILIB_FOR_TESTING;
+ std::unique_ptr<TextClassifier> classifier =
+ TextClassifier::FromPath(GetModelPath() + GetParam(), &unilib);
+ ASSERT_TRUE(classifier);
+
+ EXPECT_EQ("other",
+ FirstResult(classifier->ClassifyText(
+ "this afternoon Barack Obama gave a speech at", {15, 27})));
+ EXPECT_EQ("phone", FirstResult(classifier->ClassifyText(
+ "Call me at (800) 123-456 today", {11, 24})));
+
+ // More lines.
+ EXPECT_EQ("other",
+ FirstResult(classifier->ClassifyText(
+ "this afternoon Barack Obama gave a speech at|Visit "
+ "www.google.com every today!|Call me at (800) 123-456 today.",
+ {15, 27})));
+ EXPECT_EQ("phone",
+ FirstResult(classifier->ClassifyText(
+ "this afternoon Barack Obama gave a speech at|Visit "
+ "www.google.com every today!|Call me at (800) 123-456 today.",
+ {90, 103})));
+
+ // Single word.
+ EXPECT_EQ("other", FirstResult(classifier->ClassifyText("obama", {0, 5})));
+ EXPECT_EQ("other", FirstResult(classifier->ClassifyText("asdf", {0, 4})));
+ EXPECT_EQ("<INVALID RESULTS>",
+ FirstResult(classifier->ClassifyText("asdf", {0, 0})));
+
+ // Junk.
+ EXPECT_EQ("<INVALID RESULTS>",
+ FirstResult(classifier->ClassifyText("", {0, 0})));
+ EXPECT_EQ("<INVALID RESULTS>", FirstResult(classifier->ClassifyText(
+ "a\n\n\n\nx x x\n\n\n\n\n\n", {1, 5})));
+ // Test invalid utf8 input.
+ EXPECT_EQ("<INVALID RESULTS>", FirstResult(classifier->ClassifyText(
+ "\xf0\x9f\x98\x8b\x8b", {0, 0})));
+}
+
+TEST_P(TextClassifierTest, ClassifyTextDisabledFail) {
+ CREATE_UNILIB_FOR_TESTING;
+ const std::string test_model = ReadFile(GetModelPath() + GetParam());
+ std::unique_ptr<ModelT> unpacked_model = UnPackModel(test_model.c_str());
+
+ unpacked_model->classification_model.clear();
+ unpacked_model->triggering_options.reset(new ModelTriggeringOptionsT);
+ unpacked_model->triggering_options->enabled_modes = ModeFlag_SELECTION;
+
+ flatbuffers::FlatBufferBuilder builder;
+ builder.Finish(Model::Pack(builder, unpacked_model.get()));
+
+ std::unique_ptr<TextClassifier> classifier =
+ TextClassifier::FromUnownedBuffer(
+ reinterpret_cast<const char*>(builder.GetBufferPointer()),
+ builder.GetSize(), &unilib);
+
+ // The classification model is still needed for selection scores.
+ ASSERT_FALSE(classifier);
+}
+
+TEST_P(TextClassifierTest, ClassifyTextDisabled) {
+ CREATE_UNILIB_FOR_TESTING;
+ const std::string test_model = ReadFile(GetModelPath() + GetParam());
+ std::unique_ptr<ModelT> unpacked_model = UnPackModel(test_model.c_str());
+
+ unpacked_model->triggering_options.reset(new ModelTriggeringOptionsT);
+ unpacked_model->triggering_options->enabled_modes =
+ ModeFlag_ANNOTATION_AND_SELECTION;
+
+ flatbuffers::FlatBufferBuilder builder;
+ builder.Finish(Model::Pack(builder, unpacked_model.get()));
+
+ std::unique_ptr<TextClassifier> classifier =
+ TextClassifier::FromUnownedBuffer(
+ reinterpret_cast<const char*>(builder.GetBufferPointer()),
+ builder.GetSize(), &unilib);
+ ASSERT_TRUE(classifier);
+
+ EXPECT_THAT(
+ classifier->ClassifyText("Call me at (800) 123-456 today", {11, 24}),
+ IsEmpty());
+}
+
+TEST_P(TextClassifierTest, ClassifyTextFilteredCollections) {
+ CREATE_UNILIB_FOR_TESTING;
+ const std::string test_model = ReadFile(GetModelPath() + GetParam());
+
+ std::unique_ptr<TextClassifier> classifier =
+ TextClassifier::FromUnownedBuffer(test_model.c_str(), test_model.size(),
+ &unilib);
+ ASSERT_TRUE(classifier);
+
+ EXPECT_EQ("phone", FirstResult(classifier->ClassifyText(
+ "Call me at (800) 123-456 today", {11, 24})));
+
+ std::unique_ptr<ModelT> unpacked_model = UnPackModel(test_model.c_str());
+ unpacked_model->output_options.reset(new OutputOptionsT);
+
+ // Disable phone classification
+ unpacked_model->output_options->filtered_collections_classification.push_back(
+ "phone");
+
+ flatbuffers::FlatBufferBuilder builder;
+ builder.Finish(Model::Pack(builder, unpacked_model.get()));
+
+ classifier = TextClassifier::FromUnownedBuffer(
+ reinterpret_cast<const char*>(builder.GetBufferPointer()),
+ builder.GetSize(), &unilib);
+ ASSERT_TRUE(classifier);
+
+ EXPECT_EQ("other", FirstResult(classifier->ClassifyText(
+ "Call me at (800) 123-456 today", {11, 24})));
+
+ // Check that the address classification still passes.
+ EXPECT_EQ("address", FirstResult(classifier->ClassifyText(
+ "350 Third Street, Cambridge", {0, 27})));
+}
+
+std::unique_ptr<RegexModel_::PatternT> MakePattern(
+ const std::string& collection_name, const std::string& pattern,
+ const bool enabled_for_classification, const bool enabled_for_selection,
+ const bool enabled_for_annotation, const float score) {
+ std::unique_ptr<RegexModel_::PatternT> result(new RegexModel_::PatternT);
+ result->collection_name = collection_name;
+ result->pattern = pattern;
+ // We cannot directly operate with |= on the flag, so use an int here.
+ int enabled_modes = ModeFlag_NONE;
+ if (enabled_for_annotation) enabled_modes |= ModeFlag_ANNOTATION;
+ if (enabled_for_classification) enabled_modes |= ModeFlag_CLASSIFICATION;
+ if (enabled_for_selection) enabled_modes |= ModeFlag_SELECTION;
+ result->enabled_modes = static_cast<ModeFlag>(enabled_modes);
+ result->target_classification_score = score;
+ result->priority_score = score;
+ return result;
+}
+
+#ifdef LIBTEXTCLASSIFIER_UNILIB_ICU
+TEST_P(TextClassifierTest, ClassifyTextRegularExpression) {
+ CREATE_UNILIB_FOR_TESTING;
+ const std::string test_model = ReadFile(GetModelPath() + GetParam());
+ std::unique_ptr<ModelT> unpacked_model = UnPackModel(test_model.c_str());
+
+ // Add test regex models.
+ unpacked_model->regex_model->patterns.push_back(MakePattern(
+ "person", "Barack Obama", /*enabled_for_classification=*/true,
+ /*enabled_for_selection=*/false, /*enabled_for_annotation=*/false, 1.0));
+ unpacked_model->regex_model->patterns.push_back(MakePattern(
+ "flight", "[a-zA-Z]{2}\\d{2,4}", /*enabled_for_classification=*/true,
+ /*enabled_for_selection=*/false, /*enabled_for_annotation=*/false, 0.5));
+
+ flatbuffers::FlatBufferBuilder builder;
+ builder.Finish(Model::Pack(builder, unpacked_model.get()));
+
+ std::unique_ptr<TextClassifier> classifier =
+ TextClassifier::FromUnownedBuffer(
+ reinterpret_cast<const char*>(builder.GetBufferPointer()),
+ builder.GetSize(), &unilib);
+ ASSERT_TRUE(classifier);
+
+ EXPECT_EQ("flight",
+ FirstResult(classifier->ClassifyText(
+ "Your flight LX373 is delayed by 3 hours.", {12, 17})));
+ EXPECT_EQ("person",
+ FirstResult(classifier->ClassifyText(
+ "this afternoon Barack Obama gave a speech at", {15, 27})));
+ EXPECT_EQ("email",
+ FirstResult(classifier->ClassifyText("you@android.com", {0, 15})));
+ EXPECT_EQ("email", FirstResult(classifier->ClassifyText(
+ "Contact me at you@android.com", {14, 29})));
+
+ EXPECT_EQ("url", FirstResult(classifier->ClassifyText(
+ "Visit www.google.com every today!", {6, 20})));
+
+ EXPECT_EQ("flight", FirstResult(classifier->ClassifyText("LX 37", {0, 5})));
+ EXPECT_EQ("flight", FirstResult(classifier->ClassifyText("flight LX 37 abcd",
+ {7, 12})));
+
+ // More lines.
+ EXPECT_EQ("url",
+ FirstResult(classifier->ClassifyText(
+ "this afternoon Barack Obama gave a speech at|Visit "
+ "www.google.com every today!|Call me at (800) 123-456 today.",
+ {51, 65})));
+}
+#endif // LIBTEXTCLASSIFIER_UNILIB_ICU
+
+#ifdef LIBTEXTCLASSIFIER_UNILIB_ICU
+TEST_P(TextClassifierTest, SuggestSelectionRegularExpression) {
+ CREATE_UNILIB_FOR_TESTING;
+ const std::string test_model = ReadFile(GetModelPath() + GetParam());
+ std::unique_ptr<ModelT> unpacked_model = UnPackModel(test_model.c_str());
+
+ // Add test regex models.
+ unpacked_model->regex_model->patterns.push_back(MakePattern(
+ "person", " (Barack Obama) ", /*enabled_for_classification=*/false,
+ /*enabled_for_selection=*/true, /*enabled_for_annotation=*/false, 1.0));
+ unpacked_model->regex_model->patterns.push_back(MakePattern(
+ "flight", "([a-zA-Z]{2} ?\\d{2,4})", /*enabled_for_classification=*/false,
+ /*enabled_for_selection=*/true, /*enabled_for_annotation=*/false, 1.0));
+ unpacked_model->regex_model->patterns.back()->priority_score = 1.1;
+
+ flatbuffers::FlatBufferBuilder builder;
+ builder.Finish(Model::Pack(builder, unpacked_model.get()));
+
+ std::unique_ptr<TextClassifier> classifier =
+ TextClassifier::FromUnownedBuffer(
+ reinterpret_cast<const char*>(builder.GetBufferPointer()),
+ builder.GetSize(), &unilib);
+ ASSERT_TRUE(classifier);
+
+ // Check regular expression selection.
+ EXPECT_EQ(classifier->SuggestSelection(
+ "Your flight MA 0123 is delayed by 3 hours.", {12, 14}),
+ std::make_pair(12, 19));
+ EXPECT_EQ(classifier->SuggestSelection(
+ "this afternoon Barack Obama gave a speech at", {15, 21}),
+ std::make_pair(15, 27));
+}
+#endif // LIBTEXTCLASSIFIER_UNILIB_ICU
+
+#ifdef LIBTEXTCLASSIFIER_UNILIB_ICU
+TEST_P(TextClassifierTest,
+ SuggestSelectionRegularExpressionConflictsModelWins) {
+ const std::string test_model = ReadFile(GetModelPath() + GetParam());
+ std::unique_ptr<ModelT> unpacked_model = UnPackModel(test_model.c_str());
+
+ // Add test regex models.
+ unpacked_model->regex_model->patterns.push_back(MakePattern(
+ "person", " (Barack Obama) ", /*enabled_for_classification=*/false,
+ /*enabled_for_selection=*/true, /*enabled_for_annotation=*/false, 1.0));
+ unpacked_model->regex_model->patterns.push_back(MakePattern(
+ "flight", "([a-zA-Z]{2} ?\\d{2,4})", /*enabled_for_classification=*/false,
+ /*enabled_for_selection=*/true, /*enabled_for_annotation=*/false, 1.0));
+ unpacked_model->regex_model->patterns.back()->priority_score = 0.5;
+
+ flatbuffers::FlatBufferBuilder builder;
+ builder.Finish(Model::Pack(builder, unpacked_model.get()));
+
+ std::unique_ptr<TextClassifier> classifier =
+ TextClassifier::FromUnownedBuffer(
+ reinterpret_cast<const char*>(builder.GetBufferPointer()),
+ builder.GetSize());
+ ASSERT_TRUE(classifier);
+
+ // Check conflict resolution.
+ EXPECT_EQ(
+ classifier->SuggestSelection(
+ "saw Barack Obama today .. 350 Third Street, Cambridge, MA 0123",
+ {55, 57}),
+ std::make_pair(26, 62));
+}
+#endif // LIBTEXTCLASSIFIER_UNILIB_ICU
+
+#ifdef LIBTEXTCLASSIFIER_UNILIB_ICU
+TEST_P(TextClassifierTest,
+ SuggestSelectionRegularExpressionConflictsRegexWins) {
+ const std::string test_model = ReadFile(GetModelPath() + GetParam());
+ std::unique_ptr<ModelT> unpacked_model = UnPackModel(test_model.c_str());
+
+ // Add test regex models.
+ unpacked_model->regex_model->patterns.push_back(MakePattern(
+ "person", " (Barack Obama) ", /*enabled_for_classification=*/false,
+ /*enabled_for_selection=*/true, /*enabled_for_annotation=*/false, 1.0));
+ unpacked_model->regex_model->patterns.push_back(MakePattern(
+ "flight", "([a-zA-Z]{2} ?\\d{2,4})", /*enabled_for_classification=*/false,
+ /*enabled_for_selection=*/true, /*enabled_for_annotation=*/false, 1.0));
+ unpacked_model->regex_model->patterns.back()->priority_score = 1.1;
+
+ flatbuffers::FlatBufferBuilder builder;
+ builder.Finish(Model::Pack(builder, unpacked_model.get()));
+
+ std::unique_ptr<TextClassifier> classifier =
+ TextClassifier::FromUnownedBuffer(
+ reinterpret_cast<const char*>(builder.GetBufferPointer()),
+ builder.GetSize());
+ ASSERT_TRUE(classifier);
+
+ // Check conflict resolution.
+ EXPECT_EQ(
+ classifier->SuggestSelection(
+ "saw Barack Obama today .. 350 Third Street, Cambridge, MA 0123",
+ {55, 57}),
+ std::make_pair(55, 62));
+}
+#endif // LIBTEXTCLASSIFIER_UNILIB_ICU
+
+#ifdef LIBTEXTCLASSIFIER_UNILIB_ICU
+TEST_P(TextClassifierTest, AnnotateRegex) {
+ CREATE_UNILIB_FOR_TESTING;
+ const std::string test_model = ReadFile(GetModelPath() + GetParam());
+ std::unique_ptr<ModelT> unpacked_model = UnPackModel(test_model.c_str());
+
+ // Add test regex models.
+ unpacked_model->regex_model->patterns.push_back(MakePattern(
+ "person", " (Barack Obama) ", /*enabled_for_classification=*/false,
+ /*enabled_for_selection=*/false, /*enabled_for_annotation=*/true, 1.0));
+ unpacked_model->regex_model->patterns.push_back(MakePattern(
+ "flight", "([a-zA-Z]{2} ?\\d{2,4})", /*enabled_for_classification=*/false,
+ /*enabled_for_selection=*/false, /*enabled_for_annotation=*/true, 0.5));
+ flatbuffers::FlatBufferBuilder builder;
+ builder.Finish(Model::Pack(builder, unpacked_model.get()));
+
+ std::unique_ptr<TextClassifier> classifier =
+ TextClassifier::FromUnownedBuffer(
+ reinterpret_cast<const char*>(builder.GetBufferPointer()),
+ builder.GetSize(), &unilib);
+ ASSERT_TRUE(classifier);
+
+ const std::string test_string =
+ "& saw Barack Obama today .. 350 Third Street, Cambridge\nand my phone "
+ "number is 853 225 3556";
+ EXPECT_THAT(classifier->Annotate(test_string),
+ ElementsAreArray({
+ IsAnnotatedSpan(6, 18, "person"),
+ IsAnnotatedSpan(19, 24, "date"),
+ IsAnnotatedSpan(28, 55, "address"),
+ IsAnnotatedSpan(79, 91, "phone"),
+ }));
+}
+#endif // LIBTEXTCLASSIFIER_UNILIB_ICU
+
+TEST_P(TextClassifierTest, PhoneFiltering) {
+ CREATE_UNILIB_FOR_TESTING;
+ std::unique_ptr<TextClassifier> classifier =
+ TextClassifier::FromPath(GetModelPath() + GetParam(), &unilib);
+ ASSERT_TRUE(classifier);
+
+ EXPECT_EQ("phone", FirstResult(classifier->ClassifyText(
+ "phone: (123) 456 789", {7, 20})));
+ EXPECT_EQ("phone", FirstResult(classifier->ClassifyText(
+ "phone: (123) 456 789,0001112", {7, 25})));
+ EXPECT_EQ("other", FirstResult(classifier->ClassifyText(
+ "phone: (123) 456 789,0001112", {7, 28})));
+}
+
+TEST_P(TextClassifierTest, SuggestSelection) {
+ CREATE_UNILIB_FOR_TESTING;
+ std::unique_ptr<TextClassifier> classifier =
+ TextClassifier::FromPath(GetModelPath() + GetParam(), &unilib);
+ ASSERT_TRUE(classifier);
+
+ EXPECT_EQ(classifier->SuggestSelection(
+ "this afternoon Barack Obama gave a speech at", {15, 21}),
+ std::make_pair(15, 21));
+
+ // Try passing whole string.
+ // If more than 1 token is specified, we should return back what entered.
+ EXPECT_EQ(
+ classifier->SuggestSelection("350 Third Street, Cambridge", {0, 27}),
+ std::make_pair(0, 27));
+
+ // Single letter.
+ EXPECT_EQ(classifier->SuggestSelection("a", {0, 1}), std::make_pair(0, 1));
+
+ // Single word.
+ EXPECT_EQ(classifier->SuggestSelection("asdf", {0, 4}), std::make_pair(0, 4));
+
+ EXPECT_EQ(
+ classifier->SuggestSelection("call me at 857 225 3556 today", {11, 14}),
+ std::make_pair(11, 23));
+
+ // Unpaired bracket stripping.
+ EXPECT_EQ(
+ classifier->SuggestSelection("call me at (857) 225 3556 today", {11, 16}),
+ std::make_pair(11, 25));
+ EXPECT_EQ(classifier->SuggestSelection("call me at (857 today", {11, 15}),
+ std::make_pair(12, 15));
+ EXPECT_EQ(classifier->SuggestSelection("call me at 3556) today", {11, 16}),
+ std::make_pair(11, 15));
+ EXPECT_EQ(classifier->SuggestSelection("call me at )857( today", {11, 16}),
+ std::make_pair(12, 15));
+
+ // If the resulting selection would be empty, the original span is returned.
+ EXPECT_EQ(classifier->SuggestSelection("call me at )( today", {11, 13}),
+ std::make_pair(11, 13));
+ EXPECT_EQ(classifier->SuggestSelection("call me at ( today", {11, 12}),
+ std::make_pair(11, 12));
+ EXPECT_EQ(classifier->SuggestSelection("call me at ) today", {11, 12}),
+ std::make_pair(11, 12));
+}
+
+TEST_P(TextClassifierTest, SuggestSelectionDisabledFail) {
+ CREATE_UNILIB_FOR_TESTING;
+ const std::string test_model = ReadFile(GetModelPath() + GetParam());
+ std::unique_ptr<ModelT> unpacked_model = UnPackModel(test_model.c_str());
+
+ // Disable the selection model.
+ unpacked_model->selection_model.clear();
+ unpacked_model->triggering_options.reset(new ModelTriggeringOptionsT);
+ unpacked_model->triggering_options->enabled_modes = ModeFlag_ANNOTATION;
+
+ flatbuffers::FlatBufferBuilder builder;
+ builder.Finish(Model::Pack(builder, unpacked_model.get()));
+
+ std::unique_ptr<TextClassifier> classifier =
+ TextClassifier::FromUnownedBuffer(
+ reinterpret_cast<const char*>(builder.GetBufferPointer()),
+ builder.GetSize(), &unilib);
+ // Selection model needs to be present for annotation.
+ ASSERT_FALSE(classifier);
+}
+
+TEST_P(TextClassifierTest, SuggestSelectionDisabled) {
+ CREATE_UNILIB_FOR_TESTING;
+ const std::string test_model = ReadFile(GetModelPath() + GetParam());
+ std::unique_ptr<ModelT> unpacked_model = UnPackModel(test_model.c_str());
+
+ // Disable the selection model.
+ unpacked_model->selection_model.clear();
+ unpacked_model->triggering_options.reset(new ModelTriggeringOptionsT);
+ unpacked_model->triggering_options->enabled_modes = ModeFlag_CLASSIFICATION;
+ unpacked_model->enabled_modes = ModeFlag_CLASSIFICATION;
+
+ flatbuffers::FlatBufferBuilder builder;
+ builder.Finish(Model::Pack(builder, unpacked_model.get()));
+
+ std::unique_ptr<TextClassifier> classifier =
+ TextClassifier::FromUnownedBuffer(
+ reinterpret_cast<const char*>(builder.GetBufferPointer()),
+ builder.GetSize(), &unilib);
+ ASSERT_TRUE(classifier);
+
+ EXPECT_EQ(
+ classifier->SuggestSelection("call me at 857 225 3556 today", {11, 14}),
+ std::make_pair(11, 14));
+
+ EXPECT_EQ("phone", FirstResult(classifier->ClassifyText(
+ "call me at (800) 123-456 today", {11, 24})));
+
+ EXPECT_THAT(classifier->Annotate("call me at (800) 123-456 today"),
+ IsEmpty());
+}
+
+TEST_P(TextClassifierTest, SuggestSelectionFilteredCollections) {
+ CREATE_UNILIB_FOR_TESTING;
+ const std::string test_model = ReadFile(GetModelPath() + GetParam());
+
+ std::unique_ptr<TextClassifier> classifier =
+ TextClassifier::FromUnownedBuffer(test_model.c_str(), test_model.size(),
+ &unilib);
+ ASSERT_TRUE(classifier);
+
+ EXPECT_EQ(
+ classifier->SuggestSelection("call me at 857 225 3556 today", {11, 14}),
+ std::make_pair(11, 23));
+
+ std::unique_ptr<ModelT> unpacked_model = UnPackModel(test_model.c_str());
+ unpacked_model->output_options.reset(new OutputOptionsT);
+
+ // Disable phone selection
+ unpacked_model->output_options->filtered_collections_selection.push_back(
+ "phone");
+ // We need to force this for filtering.
+ unpacked_model->selection_options->always_classify_suggested_selection = true;
+
+ flatbuffers::FlatBufferBuilder builder;
+ builder.Finish(Model::Pack(builder, unpacked_model.get()));
+
+ classifier = TextClassifier::FromUnownedBuffer(
+ reinterpret_cast<const char*>(builder.GetBufferPointer()),
+ builder.GetSize(), &unilib);
+ ASSERT_TRUE(classifier);
+
+ EXPECT_EQ(
+ classifier->SuggestSelection("call me at 857 225 3556 today", {11, 14}),
+ std::make_pair(11, 14));
+
+ // Address selection should still work.
+ EXPECT_EQ(classifier->SuggestSelection("350 Third Street, Cambridge", {4, 9}),
+ std::make_pair(0, 27));
+}
+
+TEST_P(TextClassifierTest, SuggestSelectionsAreSymmetric) {
+ CREATE_UNILIB_FOR_TESTING;
+ std::unique_ptr<TextClassifier> classifier =
+ TextClassifier::FromPath(GetModelPath() + GetParam(), &unilib);
+ ASSERT_TRUE(classifier);
+
+ EXPECT_EQ(classifier->SuggestSelection("350 Third Street, Cambridge", {0, 3}),
+ std::make_pair(0, 27));
+ EXPECT_EQ(classifier->SuggestSelection("350 Third Street, Cambridge", {4, 9}),
+ std::make_pair(0, 27));
+ EXPECT_EQ(
+ classifier->SuggestSelection("350 Third Street, Cambridge", {10, 16}),
+ std::make_pair(0, 27));
+ EXPECT_EQ(classifier->SuggestSelection("a\nb\nc\n350 Third Street, Cambridge",
+ {16, 22}),
+ std::make_pair(6, 33));
+}
+
+TEST_P(TextClassifierTest, SuggestSelectionWithNewLine) {
+ CREATE_UNILIB_FOR_TESTING;
+ std::unique_ptr<TextClassifier> classifier =
+ TextClassifier::FromPath(GetModelPath() + GetParam(), &unilib);
+ ASSERT_TRUE(classifier);
+
+ EXPECT_EQ(classifier->SuggestSelection("abc\n857 225 3556", {4, 7}),
+ std::make_pair(4, 16));
+ EXPECT_EQ(classifier->SuggestSelection("857 225 3556\nabc", {0, 3}),
+ std::make_pair(0, 12));
+
+ SelectionOptions options;
+ EXPECT_EQ(classifier->SuggestSelection("857 225\n3556\nabc", {0, 3}, options),
+ std::make_pair(0, 7));
+}
+
+TEST_P(TextClassifierTest, SuggestSelectionWithPunctuation) {
+ CREATE_UNILIB_FOR_TESTING;
+ std::unique_ptr<TextClassifier> classifier =
+ TextClassifier::FromPath(GetModelPath() + GetParam(), &unilib);
+ ASSERT_TRUE(classifier);
+
+ // From the right.
+ EXPECT_EQ(classifier->SuggestSelection(
+ "this afternoon BarackObama, gave a speech at", {15, 26}),
+ std::make_pair(15, 26));
+
+ // From the right multiple.
+ EXPECT_EQ(classifier->SuggestSelection(
+ "this afternoon BarackObama,.,.,, gave a speech at", {15, 26}),
+ std::make_pair(15, 26));
+
+ // From the left multiple.
+ EXPECT_EQ(classifier->SuggestSelection(
+ "this afternoon ,.,.,,BarackObama gave a speech at", {21, 32}),
+ std::make_pair(21, 32));
+
+ // From both sides.
+ EXPECT_EQ(classifier->SuggestSelection(
+ "this afternoon !BarackObama,- gave a speech at", {16, 27}),
+ std::make_pair(16, 27));
+}
+
+TEST_P(TextClassifierTest, SuggestSelectionNoCrashWithJunk) {
+ CREATE_UNILIB_FOR_TESTING;
+ std::unique_ptr<TextClassifier> classifier =
+ TextClassifier::FromPath(GetModelPath() + GetParam(), &unilib);
+ ASSERT_TRUE(classifier);
+
+ // Try passing in bunch of invalid selections.
+ EXPECT_EQ(classifier->SuggestSelection("", {0, 27}), std::make_pair(0, 27));
+ EXPECT_EQ(classifier->SuggestSelection("", {-10, 27}),
+ std::make_pair(-10, 27));
+ EXPECT_EQ(classifier->SuggestSelection("Word 1 2 3 hello!", {0, 27}),
+ std::make_pair(0, 27));
+ EXPECT_EQ(classifier->SuggestSelection("Word 1 2 3 hello!", {-30, 300}),
+ std::make_pair(-30, 300));
+ EXPECT_EQ(classifier->SuggestSelection("Word 1 2 3 hello!", {-10, -1}),
+ std::make_pair(-10, -1));
+ EXPECT_EQ(classifier->SuggestSelection("Word 1 2 3 hello!", {100, 17}),
+ std::make_pair(100, 17));
+
+ // Try passing invalid utf8.
+ EXPECT_EQ(classifier->SuggestSelection("\xf0\x9f\x98\x8b\x8b", {-1, -1}),
+ std::make_pair(-1, -1));
+}
+
+TEST_P(TextClassifierTest, SuggestSelectionSelectSpace) {
+ CREATE_UNILIB_FOR_TESTING;
+ std::unique_ptr<TextClassifier> classifier =
+ TextClassifier::FromPath(GetModelPath() + GetParam(), &unilib);
+ ASSERT_TRUE(classifier);
+
+ EXPECT_EQ(
+ classifier->SuggestSelection("call me at 857 225 3556 today", {14, 15}),
+ std::make_pair(11, 23));
+ EXPECT_EQ(
+ classifier->SuggestSelection("call me at 857 225 3556 today", {10, 11}),
+ std::make_pair(10, 11));
+ EXPECT_EQ(
+ classifier->SuggestSelection("call me at 857 225 3556 today", {23, 24}),
+ std::make_pair(23, 24));
+ EXPECT_EQ(
+ classifier->SuggestSelection("call me at 857 225 3556, today", {23, 24}),
+ std::make_pair(23, 24));
+ EXPECT_EQ(classifier->SuggestSelection("call me at 857 225 3556, today",
+ {14, 17}),
+ std::make_pair(11, 25));
+ EXPECT_EQ(
+ classifier->SuggestSelection("call me at 857-225 3556, today", {14, 17}),
+ std::make_pair(11, 23));
+ EXPECT_EQ(
+ classifier->SuggestSelection(
+ "let's meet at 350 Third Street Cambridge and go there", {30, 31}),
+ std::make_pair(14, 40));
+ EXPECT_EQ(classifier->SuggestSelection("call me today", {4, 5}),
+ std::make_pair(4, 5));
+ EXPECT_EQ(classifier->SuggestSelection("call me today", {7, 8}),
+ std::make_pair(7, 8));
+
+ // With a punctuation around the selected whitespace.
+ EXPECT_EQ(
+ classifier->SuggestSelection(
+ "let's meet at 350 Third Street, Cambridge and go there", {31, 32}),
+ std::make_pair(14, 41));
+
+ // When all's whitespace, should return the original indices.
+ EXPECT_EQ(classifier->SuggestSelection(" ", {0, 1}),
+ std::make_pair(0, 1));
+ EXPECT_EQ(classifier->SuggestSelection(" ", {0, 3}),
+ std::make_pair(0, 3));
+ EXPECT_EQ(classifier->SuggestSelection(" ", {2, 3}),
+ std::make_pair(2, 3));
+ EXPECT_EQ(classifier->SuggestSelection(" ", {5, 6}),
+ std::make_pair(5, 6));
+}
+
+TEST(TextClassifierTest, SnapLeftIfWhitespaceSelection) {
+ CREATE_UNILIB_FOR_TESTING;
+ UnicodeText text;
+
+ text = UTF8ToUnicodeText("abcd efgh", /*do_copy=*/false);
+ EXPECT_EQ(internal::SnapLeftIfWhitespaceSelection({4, 5}, text, unilib),
+ std::make_pair(3, 4));
+ text = UTF8ToUnicodeText("abcd ", /*do_copy=*/false);
+ EXPECT_EQ(internal::SnapLeftIfWhitespaceSelection({4, 5}, text, unilib),
+ std::make_pair(3, 4));
+
+ // Nothing on the left.
+ text = UTF8ToUnicodeText(" efgh", /*do_copy=*/false);
+ EXPECT_EQ(internal::SnapLeftIfWhitespaceSelection({4, 5}, text, unilib),
+ std::make_pair(4, 5));
+ text = UTF8ToUnicodeText(" efgh", /*do_copy=*/false);
+ EXPECT_EQ(internal::SnapLeftIfWhitespaceSelection({0, 1}, text, unilib),
+ std::make_pair(0, 1));
+
+ // Whitespace only.
+ text = UTF8ToUnicodeText(" ", /*do_copy=*/false);
+ EXPECT_EQ(internal::SnapLeftIfWhitespaceSelection({2, 3}, text, unilib),
+ std::make_pair(2, 3));
+ text = UTF8ToUnicodeText(" ", /*do_copy=*/false);
+ EXPECT_EQ(internal::SnapLeftIfWhitespaceSelection({4, 5}, text, unilib),
+ std::make_pair(4, 5));
+ text = UTF8ToUnicodeText(" ", /*do_copy=*/false);
+ EXPECT_EQ(internal::SnapLeftIfWhitespaceSelection({0, 1}, text, unilib),
+ std::make_pair(0, 1));
+}
+
+TEST_P(TextClassifierTest, Annotate) {
+ CREATE_UNILIB_FOR_TESTING;
+ std::unique_ptr<TextClassifier> classifier =
+ TextClassifier::FromPath(GetModelPath() + GetParam(), &unilib);
+ ASSERT_TRUE(classifier);
+
+ const std::string test_string =
+ "& saw Barack Obama today .. 350 Third Street, Cambridge\nand my phone "
+ "number is 853 225 3556";
+ EXPECT_THAT(classifier->Annotate(test_string),
+ ElementsAreArray({
+#ifdef LIBTEXTCLASSIFIER_UNILIB_ICU
+ IsAnnotatedSpan(19, 24, "date"),
+#endif
+ IsAnnotatedSpan(28, 55, "address"),
+ IsAnnotatedSpan(79, 91, "phone"),
+ }));
+
+ AnnotationOptions options;
+ EXPECT_THAT(classifier->Annotate("853 225 3556", options),
+ ElementsAreArray({IsAnnotatedSpan(0, 12, "phone")}));
+ EXPECT_TRUE(classifier->Annotate("853 225\n3556", options).empty());
+
+ // Try passing invalid utf8.
+ EXPECT_TRUE(
+ classifier->Annotate("853 225 3556\n\xf0\x9f\x98\x8b\x8b", options)
+ .empty());
+}
+
+TEST_P(TextClassifierTest, AnnotateSmallBatches) {
+ CREATE_UNILIB_FOR_TESTING;
+ const std::string test_model = ReadFile(GetModelPath() + GetParam());
+ std::unique_ptr<ModelT> unpacked_model = UnPackModel(test_model.c_str());
+
+ // Set the batch size.
+ unpacked_model->selection_options->batch_size = 4;
+ flatbuffers::FlatBufferBuilder builder;
+ builder.Finish(Model::Pack(builder, unpacked_model.get()));
+
+ std::unique_ptr<TextClassifier> classifier =
+ TextClassifier::FromUnownedBuffer(
+ reinterpret_cast<const char*>(builder.GetBufferPointer()),
+ builder.GetSize(), &unilib);
+ ASSERT_TRUE(classifier);
+
+ const std::string test_string =
+ "& saw Barack Obama today .. 350 Third Street, Cambridge\nand my phone "
+ "number is 853 225 3556";
+ EXPECT_THAT(classifier->Annotate(test_string),
+ ElementsAreArray({
+#ifdef LIBTEXTCLASSIFIER_UNILIB_ICU
+ IsAnnotatedSpan(19, 24, "date"),
+#endif
+ IsAnnotatedSpan(28, 55, "address"),
+ IsAnnotatedSpan(79, 91, "phone"),
+ }));
+
+ AnnotationOptions options;
+ EXPECT_THAT(classifier->Annotate("853 225 3556", options),
+ ElementsAreArray({IsAnnotatedSpan(0, 12, "phone")}));
+ EXPECT_TRUE(classifier->Annotate("853 225\n3556", options).empty());
+}
+
+#ifdef LIBTEXTCLASSIFIER_UNILIB_ICU
+TEST_P(TextClassifierTest, AnnotateFilteringDiscardAll) {
+ CREATE_UNILIB_FOR_TESTING;
+ const std::string test_model = ReadFile(GetModelPath() + GetParam());
+ std::unique_ptr<ModelT> unpacked_model = UnPackModel(test_model.c_str());
+
+ unpacked_model->triggering_options.reset(new ModelTriggeringOptionsT);
+ // Add test threshold.
+ unpacked_model->triggering_options->min_annotate_confidence =
+ 2.f; // Discards all results.
+ flatbuffers::FlatBufferBuilder builder;
+ builder.Finish(Model::Pack(builder, unpacked_model.get()));
+
+ std::unique_ptr<TextClassifier> classifier =
+ TextClassifier::FromUnownedBuffer(
+ reinterpret_cast<const char*>(builder.GetBufferPointer()),
+ builder.GetSize(), &unilib);
+ ASSERT_TRUE(classifier);
+
+ const std::string test_string =
+ "& saw Barack Obama today .. 350 Third Street, Cambridge\nand my phone "
+ "number is 853 225 3556";
+
+ EXPECT_EQ(classifier->Annotate(test_string).size(), 1);
+}
+#endif
+
+TEST_P(TextClassifierTest, AnnotateFilteringKeepAll) {
+ CREATE_UNILIB_FOR_TESTING;
+ const std::string test_model = ReadFile(GetModelPath() + GetParam());
+ std::unique_ptr<ModelT> unpacked_model = UnPackModel(test_model.c_str());
+
+ // Add test thresholds.
+ unpacked_model->triggering_options.reset(new ModelTriggeringOptionsT);
+ unpacked_model->triggering_options->min_annotate_confidence =
+ 0.f; // Keeps all results.
+ unpacked_model->triggering_options->enabled_modes = ModeFlag_ALL;
+ flatbuffers::FlatBufferBuilder builder;
+ builder.Finish(Model::Pack(builder, unpacked_model.get()));
+
+ std::unique_ptr<TextClassifier> classifier =
+ TextClassifier::FromUnownedBuffer(
+ reinterpret_cast<const char*>(builder.GetBufferPointer()),
+ builder.GetSize(), &unilib);
+ ASSERT_TRUE(classifier);
+
+ const std::string test_string =
+ "& saw Barack Obama today .. 350 Third Street, Cambridge\nand my phone "
+ "number is 853 225 3556";
+#ifdef LIBTEXTCLASSIFIER_UNILIB_ICU
+ EXPECT_EQ(classifier->Annotate(test_string).size(), 3);
+#else
+ // In non-ICU mode there is no "date" result.
+ EXPECT_EQ(classifier->Annotate(test_string).size(), 2);
+#endif
+}
+
+TEST_P(TextClassifierTest, AnnotateDisabled) {
+ CREATE_UNILIB_FOR_TESTING;
+ const std::string test_model = ReadFile(GetModelPath() + GetParam());
+ std::unique_ptr<ModelT> unpacked_model = UnPackModel(test_model.c_str());
+
+ // Disable the model for annotation.
+ unpacked_model->enabled_modes = ModeFlag_CLASSIFICATION_AND_SELECTION;
+ flatbuffers::FlatBufferBuilder builder;
+ builder.Finish(Model::Pack(builder, unpacked_model.get()));
+
+ std::unique_ptr<TextClassifier> classifier =
+ TextClassifier::FromUnownedBuffer(
+ reinterpret_cast<const char*>(builder.GetBufferPointer()),
+ builder.GetSize(), &unilib);
+ ASSERT_TRUE(classifier);
+ const std::string test_string =
+ "& saw Barack Obama today .. 350 Third Street, Cambridge\nand my phone "
+ "number is 853 225 3556";
+ EXPECT_THAT(classifier->Annotate(test_string), IsEmpty());
+}
+
+TEST_P(TextClassifierTest, AnnotateFilteredCollections) {
+ CREATE_UNILIB_FOR_TESTING;
+ const std::string test_model = ReadFile(GetModelPath() + GetParam());
+
+ std::unique_ptr<TextClassifier> classifier =
+ TextClassifier::FromUnownedBuffer(test_model.c_str(), test_model.size(),
+ &unilib);
+ ASSERT_TRUE(classifier);
+
+ const std::string test_string =
+ "& saw Barack Obama today .. 350 Third Street, Cambridge\nand my phone "
+ "number is 853 225 3556";
+
+ EXPECT_THAT(classifier->Annotate(test_string),
+ ElementsAreArray({
+#ifdef LIBTEXTCLASSIFIER_UNILIB_ICU
+ IsAnnotatedSpan(19, 24, "date"),
+#endif
+ IsAnnotatedSpan(28, 55, "address"),
+ IsAnnotatedSpan(79, 91, "phone"),
+ }));
+
+ std::unique_ptr<ModelT> unpacked_model = UnPackModel(test_model.c_str());
+ unpacked_model->output_options.reset(new OutputOptionsT);
+
+ // Disable phone annotation
+ unpacked_model->output_options->filtered_collections_annotation.push_back(
+ "phone");
+
+ flatbuffers::FlatBufferBuilder builder;
+ builder.Finish(Model::Pack(builder, unpacked_model.get()));
+
+ classifier = TextClassifier::FromUnownedBuffer(
+ reinterpret_cast<const char*>(builder.GetBufferPointer()),
+ builder.GetSize(), &unilib);
+ ASSERT_TRUE(classifier);
+
+ EXPECT_THAT(classifier->Annotate(test_string),
+ ElementsAreArray({
+#ifdef LIBTEXTCLASSIFIER_UNILIB_ICU
+ IsAnnotatedSpan(19, 24, "date"),
+#endif
+ IsAnnotatedSpan(28, 55, "address"),
+ }));
+}
+
+#ifdef LIBTEXTCLASSIFIER_UNILIB_ICU
+TEST_P(TextClassifierTest, AnnotateFilteredCollectionsSuppress) {
+ CREATE_UNILIB_FOR_TESTING;
+ const std::string test_model = ReadFile(GetModelPath() + GetParam());
+
+ std::unique_ptr<TextClassifier> classifier =
+ TextClassifier::FromUnownedBuffer(test_model.c_str(), test_model.size(),
+ &unilib);
+ ASSERT_TRUE(classifier);
+
+ const std::string test_string =
+ "& saw Barack Obama today .. 350 Third Street, Cambridge\nand my phone "
+ "number is 853 225 3556";
+
+ EXPECT_THAT(classifier->Annotate(test_string),
+ ElementsAreArray({
+#ifdef LIBTEXTCLASSIFIER_UNILIB_ICU
+ IsAnnotatedSpan(19, 24, "date"),
+#endif
+ IsAnnotatedSpan(28, 55, "address"),
+ IsAnnotatedSpan(79, 91, "phone"),
+ }));
+
+ std::unique_ptr<ModelT> unpacked_model = UnPackModel(test_model.c_str());
+ unpacked_model->output_options.reset(new OutputOptionsT);
+
+ // We add a custom annotator that wins against the phone classification
+ // below and that we subsequently suppress.
+ unpacked_model->output_options->filtered_collections_annotation.push_back(
+ "suppress");
+
+ unpacked_model->regex_model->patterns.push_back(MakePattern(
+ "suppress", "(\\d{3} ?\\d{4})",
+ /*enabled_for_classification=*/false,
+ /*enabled_for_selection=*/false, /*enabled_for_annotation=*/true, 2.0));
+
+ flatbuffers::FlatBufferBuilder builder;
+ builder.Finish(Model::Pack(builder, unpacked_model.get()));
+
+ classifier = TextClassifier::FromUnownedBuffer(
+ reinterpret_cast<const char*>(builder.GetBufferPointer()),
+ builder.GetSize(), &unilib);
+ ASSERT_TRUE(classifier);
+
+ EXPECT_THAT(classifier->Annotate(test_string),
+ ElementsAreArray({
+ IsAnnotatedSpan(19, 24, "date"),
+ IsAnnotatedSpan(28, 55, "address"),
+ }));
+}
+#endif
+
+#ifdef LIBTEXTCLASSIFIER_CALENDAR_ICU
+TEST_P(TextClassifierTest, ClassifyTextDate) {
+ std::unique_ptr<TextClassifier> classifier =
+ TextClassifier::FromPath(GetModelPath() + GetParam());
+ EXPECT_TRUE(classifier);
+
+ std::vector<ClassificationResult> result;
+ ClassificationOptions options;
+
+ options.reference_timezone = "Europe/Zurich";
+ result = classifier->ClassifyText("january 1, 2017", {0, 15}, options);
+
+ ASSERT_EQ(result.size(), 1);
+ EXPECT_THAT(result[0].collection, "date");
+ EXPECT_EQ(result[0].datetime_parse_result.time_ms_utc, 1483225200000);
+ EXPECT_EQ(result[0].datetime_parse_result.granularity,
+ DatetimeGranularity::GRANULARITY_DAY);
+ result.clear();
+
+ options.reference_timezone = "America/Los_Angeles";
+ result = classifier->ClassifyText("march 1, 2017", {0, 13}, options);
+ ASSERT_EQ(result.size(), 1);
+ EXPECT_THAT(result[0].collection, "date");
+ EXPECT_EQ(result[0].datetime_parse_result.time_ms_utc, 1488355200000);
+ EXPECT_EQ(result[0].datetime_parse_result.granularity,
+ DatetimeGranularity::GRANULARITY_DAY);
+ result.clear();
+
+ options.reference_timezone = "America/Los_Angeles";
+ result = classifier->ClassifyText("2018/01/01 10:30:20", {0, 19}, options);
+ ASSERT_EQ(result.size(), 1);
+ EXPECT_THAT(result[0].collection, "date");
+ EXPECT_EQ(result[0].datetime_parse_result.time_ms_utc, 1514831420000);
+ EXPECT_EQ(result[0].datetime_parse_result.granularity,
+ DatetimeGranularity::GRANULARITY_SECOND);
+ result.clear();
+
+ // Date on another line.
+ options.reference_timezone = "Europe/Zurich";
+ result = classifier->ClassifyText(
+ "hello world this is the first line\n"
+ "january 1, 2017",
+ {35, 50}, options);
+ ASSERT_EQ(result.size(), 1);
+ EXPECT_THAT(result[0].collection, "date");
+ EXPECT_EQ(result[0].datetime_parse_result.time_ms_utc, 1483225200000);
+ EXPECT_EQ(result[0].datetime_parse_result.granularity,
+ DatetimeGranularity::GRANULARITY_DAY);
+}
+#endif // LIBTEXTCLASSIFIER_UNILIB_ICU
+
+#ifdef LIBTEXTCLASSIFIER_CALENDAR_ICU
+TEST_P(TextClassifierTest, ClassifyTextDatePriorities) {
+ std::unique_ptr<TextClassifier> classifier =
+ TextClassifier::FromPath(GetModelPath() + GetParam());
+ EXPECT_TRUE(classifier);
+
+ std::vector<ClassificationResult> result;
+ ClassificationOptions options;
+
+ result.clear();
+ options.reference_timezone = "Europe/Zurich";
+ options.locales = "en-US";
+ result = classifier->ClassifyText("03.05.1970", {0, 10}, options);
+
+ ASSERT_EQ(result.size(), 1);
+ EXPECT_THAT(result[0].collection, "date");
+ EXPECT_EQ(result[0].datetime_parse_result.time_ms_utc, 5439600000);
+ EXPECT_EQ(result[0].datetime_parse_result.granularity,
+ DatetimeGranularity::GRANULARITY_DAY);
+
+ result.clear();
+ options.reference_timezone = "Europe/Zurich";
+ options.locales = "de";
+ result = classifier->ClassifyText("03.05.1970", {0, 10}, options);
+
+ ASSERT_EQ(result.size(), 1);
+ EXPECT_THAT(result[0].collection, "date");
+ EXPECT_EQ(result[0].datetime_parse_result.time_ms_utc, 10537200000);
+ EXPECT_EQ(result[0].datetime_parse_result.granularity,
+ DatetimeGranularity::GRANULARITY_DAY);
+}
+#endif // LIBTEXTCLASSIFIER_UNILIB_ICU
+
+#ifdef LIBTEXTCLASSIFIER_CALENDAR_ICU
+TEST_P(TextClassifierTest, SuggestTextDateDisabled) {
+ CREATE_UNILIB_FOR_TESTING;
+ const std::string test_model = ReadFile(GetModelPath() + GetParam());
+ std::unique_ptr<ModelT> unpacked_model = UnPackModel(test_model.c_str());
+
+ // Disable the patterns for selection.
+ for (int i = 0; i < unpacked_model->datetime_model->patterns.size(); i++) {
+ unpacked_model->datetime_model->patterns[i]->enabled_modes =
+ ModeFlag_ANNOTATION_AND_CLASSIFICATION;
+ }
+ flatbuffers::FlatBufferBuilder builder;
+ builder.Finish(Model::Pack(builder, unpacked_model.get()));
+
+ std::unique_ptr<TextClassifier> classifier =
+ TextClassifier::FromUnownedBuffer(
+ reinterpret_cast<const char*>(builder.GetBufferPointer()),
+ builder.GetSize(), &unilib);
+ ASSERT_TRUE(classifier);
+ EXPECT_EQ("date",
+ FirstResult(classifier->ClassifyText("january 1, 2017", {0, 15})));
+ EXPECT_EQ(classifier->SuggestSelection("january 1, 2017", {0, 7}),
+ std::make_pair(0, 7));
+ EXPECT_THAT(classifier->Annotate("january 1, 2017"),
+ ElementsAreArray({IsAnnotatedSpan(0, 15, "date")}));
+}
+#endif // LIBTEXTCLASSIFIER_UNILIB_ICU
+
+class TestingTextClassifier : public TextClassifier {
+ public:
+ TestingTextClassifier(const std::string& model, const UniLib* unilib)
+ : TextClassifier(ViewModel(model.data(), model.size()), unilib) {}
+
+ using TextClassifier::ResolveConflicts;
+};
+
+AnnotatedSpan MakeAnnotatedSpan(CodepointSpan span,
+ const std::string& collection,
+ const float score) {
+ AnnotatedSpan result;
+ result.span = span;
+ result.classification.push_back({collection, score});
+ return result;
+}
+
+TEST(TextClassifierTest, ResolveConflictsTrivial) {
+ CREATE_UNILIB_FOR_TESTING;
+ TestingTextClassifier classifier("", &unilib);
+
+ std::vector<AnnotatedSpan> candidates{
+ {MakeAnnotatedSpan({0, 1}, "phone", 1.0)}};
+
+ std::vector<int> chosen;
+ classifier.ResolveConflicts(candidates, /*context=*/"", /*cached_tokens=*/{},
+ /*interpreter_manager=*/nullptr, &chosen);
+ EXPECT_THAT(chosen, ElementsAreArray({0}));
+}
+
+TEST(TextClassifierTest, ResolveConflictsSequence) {
+ CREATE_UNILIB_FOR_TESTING;
+ TestingTextClassifier classifier("", &unilib);
+
+ std::vector<AnnotatedSpan> candidates{{
+ MakeAnnotatedSpan({0, 1}, "phone", 1.0),
+ MakeAnnotatedSpan({1, 2}, "phone", 1.0),
+ MakeAnnotatedSpan({2, 3}, "phone", 1.0),
+ MakeAnnotatedSpan({3, 4}, "phone", 1.0),
+ MakeAnnotatedSpan({4, 5}, "phone", 1.0),
+ }};
+
+ std::vector<int> chosen;
+ classifier.ResolveConflicts(candidates, /*context=*/"", /*cached_tokens=*/{},
+ /*interpreter_manager=*/nullptr, &chosen);
+ EXPECT_THAT(chosen, ElementsAreArray({0, 1, 2, 3, 4}));
+}
+
+TEST(TextClassifierTest, ResolveConflictsThreeSpans) {
+ CREATE_UNILIB_FOR_TESTING;
+ TestingTextClassifier classifier("", &unilib);
+
+ std::vector<AnnotatedSpan> candidates{{
+ MakeAnnotatedSpan({0, 3}, "phone", 1.0),
+ MakeAnnotatedSpan({1, 5}, "phone", 0.5), // Looser!
+ MakeAnnotatedSpan({3, 7}, "phone", 1.0),
+ }};
+
+ std::vector<int> chosen;
+ classifier.ResolveConflicts(candidates, /*context=*/"", /*cached_tokens=*/{},
+ /*interpreter_manager=*/nullptr, &chosen);
+ EXPECT_THAT(chosen, ElementsAreArray({0, 2}));
+}
+
+TEST(TextClassifierTest, ResolveConflictsThreeSpansReversed) {
+ CREATE_UNILIB_FOR_TESTING;
+ TestingTextClassifier classifier("", &unilib);
+
+ std::vector<AnnotatedSpan> candidates{{
+ MakeAnnotatedSpan({0, 3}, "phone", 0.5), // Looser!
+ MakeAnnotatedSpan({1, 5}, "phone", 1.0),
+ MakeAnnotatedSpan({3, 7}, "phone", 0.6), // Looser!
+ }};
+
+ std::vector<int> chosen;
+ classifier.ResolveConflicts(candidates, /*context=*/"", /*cached_tokens=*/{},
+ /*interpreter_manager=*/nullptr, &chosen);
+ EXPECT_THAT(chosen, ElementsAreArray({1}));
+}
+
+TEST(TextClassifierTest, ResolveConflictsFiveSpans) {
+ CREATE_UNILIB_FOR_TESTING;
+ TestingTextClassifier classifier("", &unilib);
+
+ std::vector<AnnotatedSpan> candidates{{
+ MakeAnnotatedSpan({0, 3}, "phone", 0.5),
+ MakeAnnotatedSpan({1, 5}, "other", 1.0), // Looser!
+ MakeAnnotatedSpan({3, 7}, "phone", 0.6),
+ MakeAnnotatedSpan({8, 12}, "phone", 0.6), // Looser!
+ MakeAnnotatedSpan({11, 15}, "phone", 0.9),
+ }};
+
+ std::vector<int> chosen;
+ classifier.ResolveConflicts(candidates, /*context=*/"", /*cached_tokens=*/{},
+ /*interpreter_manager=*/nullptr, &chosen);
+ EXPECT_THAT(chosen, ElementsAreArray({0, 2, 4}));
+}
+
+#ifdef LIBTEXTCLASSIFIER_UNILIB_ICU
+TEST_P(TextClassifierTest, LongInput) {
+ CREATE_UNILIB_FOR_TESTING;
+ std::unique_ptr<TextClassifier> classifier =
+ TextClassifier::FromPath(GetModelPath() + GetParam(), &unilib);
+ ASSERT_TRUE(classifier);
+
+ for (const auto& type_value_pair :
+ std::vector<std::pair<std::string, std::string>>{
+ {"address", "350 Third Street, Cambridge"},
+ {"phone", "123 456-7890"},
+ {"url", "www.google.com"},
+ {"email", "someone@gmail.com"},
+ {"flight", "LX 38"},
+ {"date", "September 1, 2018"}}) {
+ const std::string input_100k = std::string(50000, ' ') +
+ type_value_pair.second +
+ std::string(50000, ' ');
+ const int value_length = type_value_pair.second.size();
+
+ EXPECT_THAT(classifier->Annotate(input_100k),
+ ElementsAreArray({IsAnnotatedSpan(50000, 50000 + value_length,
+ type_value_pair.first)}));
+ EXPECT_EQ(classifier->SuggestSelection(input_100k, {50000, 50001}),
+ std::make_pair(50000, 50000 + value_length));
+ EXPECT_EQ(type_value_pair.first,
+ FirstResult(classifier->ClassifyText(
+ input_100k, {50000, 50000 + value_length})));
+ }
+}
+#endif // LIBTEXTCLASSIFIER_UNILIB_ICU
+
+#ifdef LIBTEXTCLASSIFIER_UNILIB_ICU
+// These coarse tests are there only to make sure the execution happens in
+// reasonable amount of time.
+TEST_P(TextClassifierTest, LongInputNoResultCheck) {
+ CREATE_UNILIB_FOR_TESTING;
+ std::unique_ptr<TextClassifier> classifier =
+ TextClassifier::FromPath(GetModelPath() + GetParam(), &unilib);
+ ASSERT_TRUE(classifier);
+
+ for (const std::string& value :
+ std::vector<std::string>{"http://www.aaaaaaaaaaaaaaaaaaaa.com "}) {
+ const std::string input_100k =
+ std::string(50000, ' ') + value + std::string(50000, ' ');
+ const int value_length = value.size();
+
+ classifier->Annotate(input_100k);
+ classifier->SuggestSelection(input_100k, {50000, 50001});
+ classifier->ClassifyText(input_100k, {50000, 50000 + value_length});
+ }
+}
+#endif // LIBTEXTCLASSIFIER_UNILIB_ICU
+
+#ifdef LIBTEXTCLASSIFIER_UNILIB_ICU
+TEST_P(TextClassifierTest, MaxTokenLength) {
+ CREATE_UNILIB_FOR_TESTING;
+ const std::string test_model = ReadFile(GetModelPath() + GetParam());
+ std::unique_ptr<ModelT> unpacked_model = UnPackModel(test_model.c_str());
+
+ std::unique_ptr<TextClassifier> classifier;
+
+ // With unrestricted number of tokens should behave normally.
+ unpacked_model->classification_options->max_num_tokens = -1;
+
+ flatbuffers::FlatBufferBuilder builder;
+ builder.Finish(Model::Pack(builder, unpacked_model.get()));
+ classifier = TextClassifier::FromUnownedBuffer(
+ reinterpret_cast<const char*>(builder.GetBufferPointer()),
+ builder.GetSize(), &unilib);
+ ASSERT_TRUE(classifier);
+
+ EXPECT_EQ(FirstResult(classifier->ClassifyText(
+ "I live at 350 Third Street, Cambridge.", {10, 37})),
+ "address");
+
+ // Raise the maximum number of tokens to suppress the classification.
+ unpacked_model->classification_options->max_num_tokens = 3;
+
+ flatbuffers::FlatBufferBuilder builder2;
+ builder2.Finish(Model::Pack(builder2, unpacked_model.get()));
+ classifier = TextClassifier::FromUnownedBuffer(
+ reinterpret_cast<const char*>(builder2.GetBufferPointer()),
+ builder2.GetSize(), &unilib);
+ ASSERT_TRUE(classifier);
+
+ EXPECT_EQ(FirstResult(classifier->ClassifyText(
+ "I live at 350 Third Street, Cambridge.", {10, 37})),
+ "other");
+}
+#endif // LIBTEXTCLASSIFIER_UNILIB_ICU
+
+#ifdef LIBTEXTCLASSIFIER_UNILIB_ICU
+TEST_P(TextClassifierTest, MinAddressTokenLength) {
+ CREATE_UNILIB_FOR_TESTING;
+ const std::string test_model = ReadFile(GetModelPath() + GetParam());
+ std::unique_ptr<ModelT> unpacked_model = UnPackModel(test_model.c_str());
+
+ std::unique_ptr<TextClassifier> classifier;
+
+ // With unrestricted number of address tokens should behave normally.
+ unpacked_model->classification_options->address_min_num_tokens = 0;
+
+ flatbuffers::FlatBufferBuilder builder;
+ builder.Finish(Model::Pack(builder, unpacked_model.get()));
+ classifier = TextClassifier::FromUnownedBuffer(
+ reinterpret_cast<const char*>(builder.GetBufferPointer()),
+ builder.GetSize(), &unilib);
+ ASSERT_TRUE(classifier);
+
+ EXPECT_EQ(FirstResult(classifier->ClassifyText(
+ "I live at 350 Third Street, Cambridge.", {10, 37})),
+ "address");
+
+ // Raise number of address tokens to suppress the address classification.
+ unpacked_model->classification_options->address_min_num_tokens = 5;
+
+ flatbuffers::FlatBufferBuilder builder2;
+ builder2.Finish(Model::Pack(builder2, unpacked_model.get()));
+ classifier = TextClassifier::FromUnownedBuffer(
+ reinterpret_cast<const char*>(builder2.GetBufferPointer()),
+ builder2.GetSize(), &unilib);
+ ASSERT_TRUE(classifier);
+
+ EXPECT_EQ(FirstResult(classifier->ClassifyText(
+ "I live at 350 Third Street, Cambridge.", {10, 37})),
+ "other");
+}
+#endif // LIBTEXTCLASSIFIER_UNILIB_ICU
+
+} // namespace
+} // namespace libtextclassifier2
diff --git a/textclassifier_jni.cc b/textclassifier_jni.cc
index 8740f4c..29cf745 100644
--- a/textclassifier_jni.cc
+++ b/textclassifier_jni.cc
@@ -14,104 +14,184 @@
* limitations under the License.
*/
-// Simple JNI wrapper for the SmartSelection library.
+// JNI wrapper for the TextClassifier.
#include "textclassifier_jni.h"
#include <jni.h>
+#include <type_traits>
#include <vector>
-#include "lang_id/lang-id.h"
-#include "smartselect/text-classification-model.h"
+#include "text-classifier.h"
+#include "util/base/integral_types.h"
#include "util/java/scoped_local_ref.h"
+#include "util/java/string_utils.h"
+#include "util/memory/mmap.h"
+#include "util/utf8/unilib.h"
-using libtextclassifier::ModelOptions;
-using libtextclassifier::TextClassificationModel;
-using libtextclassifier::nlp_core::lang_id::LangId;
+using libtextclassifier2::AnnotatedSpan;
+using libtextclassifier2::AnnotationOptions;
+using libtextclassifier2::ClassificationOptions;
+using libtextclassifier2::ClassificationResult;
+using libtextclassifier2::CodepointSpan;
+using libtextclassifier2::JStringToUtf8String;
+using libtextclassifier2::Model;
+using libtextclassifier2::ScopedLocalRef;
+using libtextclassifier2::SelectionOptions;
+using libtextclassifier2::TextClassifier;
+#ifdef LIBTEXTCLASSIFIER_UNILIB_JAVAICU
+using libtextclassifier2::UniLib;
+#endif
+
+namespace libtextclassifier2 {
+
+using libtextclassifier2::CodepointSpan;
namespace {
-bool JStringToUtf8String(JNIEnv* env, const jstring& jstr,
- std::string* result) {
- if (jstr == nullptr) {
- *result = std::string();
- return false;
- }
-
- jclass string_class = env->FindClass("java/lang/String");
- if (!string_class) {
- TC_LOG(ERROR) << "Can't find String class";
- return false;
- }
-
- jmethodID get_bytes_id =
- env->GetMethodID(string_class, "getBytes", "(Ljava/lang/String;)[B");
-
- jstring encoding = env->NewStringUTF("UTF-8");
- jbyteArray array = reinterpret_cast<jbyteArray>(
- env->CallObjectMethod(jstr, get_bytes_id, encoding));
-
- jbyte* const array_bytes = env->GetByteArrayElements(array, JNI_FALSE);
- int length = env->GetArrayLength(array);
-
- *result = std::string(reinterpret_cast<char*>(array_bytes), length);
-
- // Release the array.
- env->ReleaseByteArrayElements(array, array_bytes, JNI_ABORT);
- env->DeleteLocalRef(array);
- env->DeleteLocalRef(string_class);
- env->DeleteLocalRef(encoding);
-
- return true;
-}
-
std::string ToStlString(JNIEnv* env, const jstring& str) {
std::string result;
JStringToUtf8String(env, str, &result);
return result;
}
-jobjectArray ScoredStringsToJObjectArray(
- JNIEnv* env, const std::string& result_class_name,
- const std::vector<std::pair<std::string, float>>& classification_result) {
- jclass result_class = env->FindClass(result_class_name.c_str());
+jobjectArray ClassificationResultsToJObjectArray(
+ JNIEnv* env,
+ const std::vector<ClassificationResult>& classification_result) {
+ const ScopedLocalRef<jclass> result_class(
+ env->FindClass(TC_PACKAGE_PATH TC_CLASS_NAME_STR "$ClassificationResult"),
+ env);
if (!result_class) {
- TC_LOG(ERROR) << "Couldn't find result class: " << result_class_name;
+ TC_LOG(ERROR) << "Couldn't find ClassificationResult class.";
+ return nullptr;
+ }
+ const ScopedLocalRef<jclass> datetime_parse_class(
+ env->FindClass(TC_PACKAGE_PATH TC_CLASS_NAME_STR "$DatetimeResult"), env);
+ if (!datetime_parse_class) {
+ TC_LOG(ERROR) << "Couldn't find DatetimeResult class.";
return nullptr;
}
- jmethodID result_class_constructor =
- env->GetMethodID(result_class, "<init>", "(Ljava/lang/String;F)V");
+ const jmethodID result_class_constructor =
+ env->GetMethodID(result_class.get(), "<init>",
+ "(Ljava/lang/String;FL" TC_PACKAGE_PATH TC_CLASS_NAME_STR
+ "$DatetimeResult;)V");
+ const jmethodID datetime_parse_class_constructor =
+ env->GetMethodID(datetime_parse_class.get(), "<init>", "(JI)V");
- jobjectArray results =
- env->NewObjectArray(classification_result.size(), result_class, nullptr);
-
+ const jobjectArray results = env->NewObjectArray(classification_result.size(),
+ result_class.get(), nullptr);
for (int i = 0; i < classification_result.size(); i++) {
jstring row_string =
- env->NewStringUTF(classification_result[i].first.c_str());
+ env->NewStringUTF(classification_result[i].collection.c_str());
+ jobject row_datetime_parse = nullptr;
+ if (classification_result[i].datetime_parse_result.IsSet()) {
+ row_datetime_parse = env->NewObject(
+ datetime_parse_class.get(), datetime_parse_class_constructor,
+ classification_result[i].datetime_parse_result.time_ms_utc,
+ classification_result[i].datetime_parse_result.granularity);
+ }
jobject result =
- env->NewObject(result_class, result_class_constructor, row_string,
- static_cast<jfloat>(classification_result[i].second));
+ env->NewObject(result_class.get(), result_class_constructor, row_string,
+ static_cast<jfloat>(classification_result[i].score),
+ row_datetime_parse);
env->SetObjectArrayElement(results, i, result);
env->DeleteLocalRef(result);
}
- env->DeleteLocalRef(result_class);
return results;
}
-} // namespace
+template <typename T, typename F>
+std::pair<bool, T> CallJniMethod0(JNIEnv* env, jobject object,
+ jclass class_object, F function,
+ const std::string& method_name,
+ const std::string& return_java_type) {
+ const jmethodID method = env->GetMethodID(class_object, method_name.c_str(),
+ ("()" + return_java_type).c_str());
+ if (!method) {
+ return std::make_pair(false, T());
+ }
+ return std::make_pair(true, (env->*function)(object, method));
+}
-namespace libtextclassifier {
+SelectionOptions FromJavaSelectionOptions(JNIEnv* env, jobject joptions) {
+ if (!joptions) {
+ return {};
+ }
-using libtextclassifier::CodepointSpan;
+ const ScopedLocalRef<jclass> options_class(
+ env->FindClass(TC_PACKAGE_PATH TC_CLASS_NAME_STR "$SelectionOptions"),
+ env);
+ const std::pair<bool, jobject> status_or_locales = CallJniMethod0<jobject>(
+ env, joptions, options_class.get(), &JNIEnv::CallObjectMethod,
+ "getLocales", "Ljava/lang/String;");
+ if (!status_or_locales.first) {
+ return {};
+ }
-namespace {
+ SelectionOptions options;
+ options.locales =
+ ToStlString(env, reinterpret_cast<jstring>(status_or_locales.second));
+
+ return options;
+}
+
+template <typename T>
+T FromJavaOptionsInternal(JNIEnv* env, jobject joptions,
+ const std::string& class_name) {
+ if (!joptions) {
+ return {};
+ }
+
+ const ScopedLocalRef<jclass> options_class(env->FindClass(class_name.c_str()),
+ env);
+ if (!options_class) {
+ return {};
+ }
+
+ const std::pair<bool, jobject> status_or_locales = CallJniMethod0<jobject>(
+ env, joptions, options_class.get(), &JNIEnv::CallObjectMethod,
+ "getLocale", "Ljava/lang/String;");
+ const std::pair<bool, jobject> status_or_reference_timezone =
+ CallJniMethod0<jobject>(env, joptions, options_class.get(),
+ &JNIEnv::CallObjectMethod, "getReferenceTimezone",
+ "Ljava/lang/String;");
+ const std::pair<bool, int64> status_or_reference_time_ms_utc =
+ CallJniMethod0<int64>(env, joptions, options_class.get(),
+ &JNIEnv::CallLongMethod, "getReferenceTimeMsUtc",
+ "J");
+
+ if (!status_or_locales.first || !status_or_reference_timezone.first ||
+ !status_or_reference_time_ms_utc.first) {
+ return {};
+ }
+
+ T options;
+ options.locales =
+ ToStlString(env, reinterpret_cast<jstring>(status_or_locales.second));
+ options.reference_timezone = ToStlString(
+ env, reinterpret_cast<jstring>(status_or_reference_timezone.second));
+ options.reference_time_ms_utc = status_or_reference_time_ms_utc.second;
+ return options;
+}
+
+ClassificationOptions FromJavaClassificationOptions(JNIEnv* env,
+ jobject joptions) {
+ return FromJavaOptionsInternal<ClassificationOptions>(
+ env, joptions,
+ TC_PACKAGE_PATH TC_CLASS_NAME_STR "$ClassificationOptions");
+}
+
+AnnotationOptions FromJavaAnnotationOptions(JNIEnv* env, jobject joptions) {
+ return FromJavaOptionsInternal<AnnotationOptions>(
+ env, joptions, TC_PACKAGE_PATH TC_CLASS_NAME_STR "$AnnotationOptions");
+}
CodepointSpan ConvertIndicesBMPUTF8(const std::string& utf8_str,
CodepointSpan orig_indices,
bool from_utf8) {
- const libtextclassifier::UnicodeText unicode_str =
- libtextclassifier::UTF8ToUnicodeText(utf8_str, /*do_copy=*/false);
+ const libtextclassifier2::UnicodeText unicode_str =
+ libtextclassifier2::UTF8ToUnicodeText(utf8_str, /*do_copy=*/false);
int unicode_index = 0;
int bmp_index = 0;
@@ -155,83 +235,142 @@
} // namespace
CodepointSpan ConvertIndicesBMPToUTF8(const std::string& utf8_str,
- CodepointSpan orig_indices) {
- return ConvertIndicesBMPUTF8(utf8_str, orig_indices, /*from_utf8=*/false);
+ CodepointSpan bmp_indices) {
+ return ConvertIndicesBMPUTF8(utf8_str, bmp_indices, /*from_utf8=*/false);
}
CodepointSpan ConvertIndicesUTF8ToBMP(const std::string& utf8_str,
- CodepointSpan orig_indices) {
- return ConvertIndicesBMPUTF8(utf8_str, orig_indices, /*from_utf8=*/true);
+ CodepointSpan utf8_indices) {
+ return ConvertIndicesBMPUTF8(utf8_str, utf8_indices, /*from_utf8=*/true);
}
-} // namespace libtextclassifier
-
-using libtextclassifier::CodepointSpan;
-using libtextclassifier::ConvertIndicesBMPToUTF8;
-using libtextclassifier::ConvertIndicesUTF8ToBMP;
-using libtextclassifier::ScopedLocalRef;
-
-JNI_METHOD(jlong, SmartSelection, nativeNew)
-(JNIEnv* env, jobject thiz, jint fd) {
- TextClassificationModel* model = new TextClassificationModel(fd);
- return reinterpret_cast<jlong>(model);
-}
-
-JNI_METHOD(jlong, SmartSelection, nativeNewFromPath)
-(JNIEnv* env, jobject thiz, jstring path) {
- const std::string path_str = ToStlString(env, path);
- TextClassificationModel* model = new TextClassificationModel(path_str);
- return reinterpret_cast<jlong>(model);
-}
-
-JNI_METHOD(jlong, SmartSelection, nativeNewFromAssetFileDescriptor)
-(JNIEnv* env, jobject thiz, jobject afd, jlong offset, jlong size) {
+jint GetFdFromAssetFileDescriptor(JNIEnv* env, jobject afd) {
// Get system-level file descriptor from AssetFileDescriptor.
ScopedLocalRef<jclass> afd_class(
env->FindClass("android/content/res/AssetFileDescriptor"), env);
if (afd_class == nullptr) {
- TC_LOG(ERROR) << "Couln't find AssetFileDescriptor.";
+ TC_LOG(ERROR) << "Couldn't find AssetFileDescriptor.";
return reinterpret_cast<jlong>(nullptr);
}
jmethodID afd_class_getFileDescriptor = env->GetMethodID(
afd_class.get(), "getFileDescriptor", "()Ljava/io/FileDescriptor;");
if (afd_class_getFileDescriptor == nullptr) {
- TC_LOG(ERROR) << "Couln't find getFileDescriptor.";
+ TC_LOG(ERROR) << "Couldn't find getFileDescriptor.";
return reinterpret_cast<jlong>(nullptr);
}
ScopedLocalRef<jclass> fd_class(env->FindClass("java/io/FileDescriptor"),
env);
if (fd_class == nullptr) {
- TC_LOG(ERROR) << "Couln't find FileDescriptor.";
+ TC_LOG(ERROR) << "Couldn't find FileDescriptor.";
return reinterpret_cast<jlong>(nullptr);
}
jfieldID fd_class_descriptor =
env->GetFieldID(fd_class.get(), "descriptor", "I");
if (fd_class_descriptor == nullptr) {
- TC_LOG(ERROR) << "Couln't find descriptor.";
+ TC_LOG(ERROR) << "Couldn't find descriptor.";
return reinterpret_cast<jlong>(nullptr);
}
jobject bundle_jfd = env->CallObjectMethod(afd, afd_class_getFileDescriptor);
- jint bundle_cfd = env->GetIntField(bundle_jfd, fd_class_descriptor);
-
- TextClassificationModel* model =
- new TextClassificationModel(bundle_cfd, offset, size);
- return reinterpret_cast<jlong>(model);
+ return env->GetIntField(bundle_jfd, fd_class_descriptor);
}
-JNI_METHOD(jintArray, SmartSelection, nativeSuggest)
+jstring GetLocalesFromMmap(JNIEnv* env, libtextclassifier2::ScopedMmap* mmap) {
+ if (!mmap->handle().ok()) {
+ return env->NewStringUTF("");
+ }
+ const Model* model = libtextclassifier2::ViewModel(
+ mmap->handle().start(), mmap->handle().num_bytes());
+ if (!model || !model->locales()) {
+ return env->NewStringUTF("");
+ }
+ return env->NewStringUTF(model->locales()->c_str());
+}
+
+jint GetVersionFromMmap(JNIEnv* env, libtextclassifier2::ScopedMmap* mmap) {
+ if (!mmap->handle().ok()) {
+ return 0;
+ }
+ const Model* model = libtextclassifier2::ViewModel(
+ mmap->handle().start(), mmap->handle().num_bytes());
+ if (!model) {
+ return 0;
+ }
+ return model->version();
+}
+
+jstring GetNameFromMmap(JNIEnv* env, libtextclassifier2::ScopedMmap* mmap) {
+ if (!mmap->handle().ok()) {
+ return env->NewStringUTF("");
+ }
+ const Model* model = libtextclassifier2::ViewModel(
+ mmap->handle().start(), mmap->handle().num_bytes());
+ if (!model || !model->name()) {
+ return env->NewStringUTF("");
+ }
+ return env->NewStringUTF(model->name()->c_str());
+}
+
+} // namespace libtextclassifier2
+
+using libtextclassifier2::ClassificationResultsToJObjectArray;
+using libtextclassifier2::ConvertIndicesBMPToUTF8;
+using libtextclassifier2::ConvertIndicesUTF8ToBMP;
+using libtextclassifier2::FromJavaAnnotationOptions;
+using libtextclassifier2::FromJavaClassificationOptions;
+using libtextclassifier2::FromJavaSelectionOptions;
+using libtextclassifier2::ToStlString;
+
+JNI_METHOD(jlong, TC_CLASS_NAME, nativeNew)
+(JNIEnv* env, jobject thiz, jint fd) {
+#ifdef LIBTEXTCLASSIFIER_UNILIB_JAVAICU
+ return reinterpret_cast<jlong>(
+ TextClassifier::FromFileDescriptor(fd).release(), new UniLib(env));
+#else
+ return reinterpret_cast<jlong>(
+ TextClassifier::FromFileDescriptor(fd).release());
+#endif
+}
+
+JNI_METHOD(jlong, TC_CLASS_NAME, nativeNewFromPath)
+(JNIEnv* env, jobject thiz, jstring path) {
+ const std::string path_str = ToStlString(env, path);
+#ifdef LIBTEXTCLASSIFIER_UNILIB_JAVAICU
+ return reinterpret_cast<jlong>(
+ TextClassifier::FromPath(path_str, new UniLib(env)).release());
+#else
+ return reinterpret_cast<jlong>(TextClassifier::FromPath(path_str).release());
+#endif
+}
+
+JNI_METHOD(jlong, TC_CLASS_NAME, nativeNewFromAssetFileDescriptor)
+(JNIEnv* env, jobject thiz, jobject afd, jlong offset, jlong size) {
+ const jint fd = libtextclassifier2::GetFdFromAssetFileDescriptor(env, afd);
+#ifdef LIBTEXTCLASSIFIER_UNILIB_JAVAICU
+ return reinterpret_cast<jlong>(
+ TextClassifier::FromFileDescriptor(fd, offset, size, new UniLib(env))
+ .release());
+#else
+ return reinterpret_cast<jlong>(
+ TextClassifier::FromFileDescriptor(fd, offset, size).release());
+#endif
+}
+
+JNI_METHOD(jintArray, TC_CLASS_NAME, nativeSuggestSelection)
(JNIEnv* env, jobject thiz, jlong ptr, jstring context, jint selection_begin,
- jint selection_end) {
- TextClassificationModel* model =
- reinterpret_cast<TextClassificationModel*>(ptr);
+ jint selection_end, jobject options) {
+ if (!ptr) {
+ return nullptr;
+ }
+
+ TextClassifier* model = reinterpret_cast<TextClassifier*>(ptr);
const std::string context_utf8 = ToStlString(env, context);
CodepointSpan input_indices =
ConvertIndicesBMPToUTF8(context_utf8, {selection_begin, selection_end});
- CodepointSpan selection =
- model->SuggestSelection(context_utf8, input_indices);
+ CodepointSpan selection = model->SuggestSelection(
+ context_utf8, input_indices, FromJavaSelectionOptions(env, options));
selection = ConvertIndicesUTF8ToBMP(context_utf8, selection);
jintArray result = env->NewIntArray(2);
@@ -240,39 +379,45 @@
return result;
}
-JNI_METHOD(jobjectArray, SmartSelection, nativeClassifyText)
+JNI_METHOD(jobjectArray, TC_CLASS_NAME, nativeClassifyText)
(JNIEnv* env, jobject thiz, jlong ptr, jstring context, jint selection_begin,
- jint selection_end, jint input_flags) {
- TextClassificationModel* ff_model =
- reinterpret_cast<TextClassificationModel*>(ptr);
- const std::vector<std::pair<std::string, float>> classification_result =
- ff_model->ClassifyText(ToStlString(env, context),
- {selection_begin, selection_end}, input_flags);
+ jint selection_end, jobject options) {
+ if (!ptr) {
+ return nullptr;
+ }
+ TextClassifier* ff_model = reinterpret_cast<TextClassifier*>(ptr);
- return ScoredStringsToJObjectArray(
- env, TC_PACKAGE_PATH "SmartSelection$ClassificationResult",
- classification_result);
+ const std::string context_utf8 = ToStlString(env, context);
+ const CodepointSpan input_indices =
+ ConvertIndicesBMPToUTF8(context_utf8, {selection_begin, selection_end});
+ const std::vector<ClassificationResult> classification_result =
+ ff_model->ClassifyText(context_utf8, input_indices,
+ FromJavaClassificationOptions(env, options));
+
+ return ClassificationResultsToJObjectArray(env, classification_result);
}
-JNI_METHOD(jobjectArray, SmartSelection, nativeAnnotate)
-(JNIEnv* env, jobject thiz, jlong ptr, jstring context) {
- TextClassificationModel* model =
- reinterpret_cast<TextClassificationModel*>(ptr);
+JNI_METHOD(jobjectArray, TC_CLASS_NAME, nativeAnnotate)
+(JNIEnv* env, jobject thiz, jlong ptr, jstring context, jobject options) {
+ if (!ptr) {
+ return nullptr;
+ }
+ TextClassifier* model = reinterpret_cast<TextClassifier*>(ptr);
std::string context_utf8 = ToStlString(env, context);
- std::vector<TextClassificationModel::AnnotatedSpan> annotations =
- model->Annotate(context_utf8);
+ std::vector<AnnotatedSpan> annotations =
+ model->Annotate(context_utf8, FromJavaAnnotationOptions(env, options));
jclass result_class =
- env->FindClass(TC_PACKAGE_PATH "SmartSelection$AnnotatedSpan");
+ env->FindClass(TC_PACKAGE_PATH TC_CLASS_NAME_STR "$AnnotatedSpan");
if (!result_class) {
TC_LOG(ERROR) << "Couldn't find result class: "
- << TC_PACKAGE_PATH "SmartSelection$AnnotatedSpan";
+ << TC_PACKAGE_PATH TC_CLASS_NAME_STR "$AnnotatedSpan";
return nullptr;
}
jmethodID result_class_constructor = env->GetMethodID(
result_class, "<init>",
- "(II[L" TC_PACKAGE_PATH "SmartSelection$ClassificationResult;)V");
+ "(II[L" TC_PACKAGE_PATH TC_CLASS_NAME_STR "$ClassificationResult;)V");
jobjectArray results =
env->NewObjectArray(annotations.size(), result_class, nullptr);
@@ -283,9 +428,9 @@
jobject result = env->NewObject(
result_class, result_class_constructor,
static_cast<jint>(span_bmp.first), static_cast<jint>(span_bmp.second),
- ScoredStringsToJObjectArray(
- env, TC_PACKAGE_PATH "SmartSelection$ClassificationResult",
- annotations[i].classification));
+ ClassificationResultsToJObjectArray(env,
+
+ annotations[i].classification));
env->SetObjectArrayElement(results, i, result);
env->DeleteLocalRef(result);
}
@@ -293,58 +438,59 @@
return results;
}
-JNI_METHOD(void, SmartSelection, nativeClose)
+JNI_METHOD(void, TC_CLASS_NAME, nativeClose)
(JNIEnv* env, jobject thiz, jlong ptr) {
- TextClassificationModel* model =
- reinterpret_cast<TextClassificationModel*>(ptr);
+ TextClassifier* model = reinterpret_cast<TextClassifier*>(ptr);
delete model;
}
-JNI_METHOD(jstring, SmartSelection, nativeGetLanguage)
+JNI_METHOD(jstring, TC_CLASS_NAME, nativeGetLanguage)
(JNIEnv* env, jobject clazz, jint fd) {
- ModelOptions model_options;
- if (ReadSelectionModelOptions(fd, &model_options)) {
- return env->NewStringUTF(model_options.language().c_str());
- } else {
- return env->NewStringUTF("UNK");
- }
+ TC_LOG(WARNING) << "Using deprecated getLanguage().";
+ return JNI_METHOD_NAME(TC_CLASS_NAME, nativeGetLocales)(env, clazz, fd);
}
-JNI_METHOD(jint, SmartSelection, nativeGetVersion)
+JNI_METHOD(jstring, TC_CLASS_NAME, nativeGetLocales)
(JNIEnv* env, jobject clazz, jint fd) {
- ModelOptions model_options;
- if (ReadSelectionModelOptions(fd, &model_options)) {
- return model_options.version();
- } else {
- return -1;
- }
+ const std::unique_ptr<libtextclassifier2::ScopedMmap> mmap(
+ new libtextclassifier2::ScopedMmap(fd));
+ return GetLocalesFromMmap(env, mmap.get());
}
-#ifndef LIBTEXTCLASSIFIER_DISABLE_LANG_ID
-JNI_METHOD(jlong, LangId, nativeNew)
-(JNIEnv* env, jobject thiz, jint fd) {
- return reinterpret_cast<jlong>(new LangId(fd));
+JNI_METHOD(jstring, TC_CLASS_NAME, nativeGetLocalesFromAssetFileDescriptor)
+(JNIEnv* env, jobject thiz, jobject afd, jlong offset, jlong size) {
+ const jint fd = libtextclassifier2::GetFdFromAssetFileDescriptor(env, afd);
+ const std::unique_ptr<libtextclassifier2::ScopedMmap> mmap(
+ new libtextclassifier2::ScopedMmap(fd, offset, size));
+ return GetLocalesFromMmap(env, mmap.get());
}
-JNI_METHOD(jobjectArray, LangId, nativeFindLanguages)
-(JNIEnv* env, jobject thiz, jlong ptr, jstring text) {
- LangId* lang_id = reinterpret_cast<LangId*>(ptr);
- const std::vector<std::pair<std::string, float>> scored_languages =
- lang_id->FindLanguages(ToStlString(env, text));
-
- return ScoredStringsToJObjectArray(
- env, TC_PACKAGE_PATH "LangId$ClassificationResult", scored_languages);
-}
-
-JNI_METHOD(void, LangId, nativeClose)
-(JNIEnv* env, jobject thiz, jlong ptr) {
- LangId* lang_id = reinterpret_cast<LangId*>(ptr);
- delete lang_id;
-}
-
-JNI_METHOD(int, LangId, nativeGetVersion)
+JNI_METHOD(jint, TC_CLASS_NAME, nativeGetVersion)
(JNIEnv* env, jobject clazz, jint fd) {
- std::unique_ptr<LangId> lang_id(new LangId(fd));
- return lang_id->version();
+ const std::unique_ptr<libtextclassifier2::ScopedMmap> mmap(
+ new libtextclassifier2::ScopedMmap(fd));
+ return GetVersionFromMmap(env, mmap.get());
}
-#endif
+
+JNI_METHOD(jint, TC_CLASS_NAME, nativeGetVersionFromAssetFileDescriptor)
+(JNIEnv* env, jobject thiz, jobject afd, jlong offset, jlong size) {
+ const jint fd = libtextclassifier2::GetFdFromAssetFileDescriptor(env, afd);
+ const std::unique_ptr<libtextclassifier2::ScopedMmap> mmap(
+ new libtextclassifier2::ScopedMmap(fd, offset, size));
+ return GetVersionFromMmap(env, mmap.get());
+}
+
+JNI_METHOD(jstring, TC_CLASS_NAME, nativeGetName)
+(JNIEnv* env, jobject clazz, jint fd) {
+ const std::unique_ptr<libtextclassifier2::ScopedMmap> mmap(
+ new libtextclassifier2::ScopedMmap(fd));
+ return GetNameFromMmap(env, mmap.get());
+}
+
+JNI_METHOD(jstring, TC_CLASS_NAME, nativeGetNameFromAssetFileDescriptor)
+(JNIEnv* env, jobject thiz, jobject afd, jlong offset, jlong size) {
+ const jint fd = libtextclassifier2::GetFdFromAssetFileDescriptor(env, afd);
+ const std::unique_ptr<libtextclassifier2::ScopedMmap> mmap(
+ new libtextclassifier2::ScopedMmap(fd, offset, size));
+ return GetNameFromMmap(env, mmap.get());
+}
diff --git a/textclassifier_jni.h b/textclassifier_jni.h
index 1709ff4..d6e742e 100644
--- a/textclassifier_jni.h
+++ b/textclassifier_jni.h
@@ -20,89 +20,115 @@
#include <jni.h>
#include <string>
-#include "smartselect/types.h"
+#include "types.h"
+
+// When we use a macro as an argument for a macro, an additional level of
+// indirection is needed, if the macro argument is used with # or ##.
+#define ADD_QUOTES_HELPER(TOKEN) #TOKEN
+#define ADD_QUOTES(TOKEN) ADD_QUOTES_HELPER(TOKEN)
#ifndef TC_PACKAGE_NAME
#define TC_PACKAGE_NAME android_view_textclassifier
#endif
+
+#ifndef TC_CLASS_NAME
+#define TC_CLASS_NAME TextClassifierImplNative
+#endif
+#define TC_CLASS_NAME_STR ADD_QUOTES(TC_CLASS_NAME)
+
#ifndef TC_PACKAGE_PATH
#define TC_PACKAGE_PATH "android/view/textclassifier/"
#endif
+#define JNI_METHOD_NAME_INTERNAL(package_name, class_name, method_name) \
+ Java_##package_name##_##class_name##_##method_name
+
#define JNI_METHOD_PRIMITIVE(return_type, package_name, class_name, \
method_name) \
- JNIEXPORT return_type JNICALL \
- Java_##package_name##_##class_name##_##method_name
+ JNIEXPORT return_type JNICALL JNI_METHOD_NAME_INTERNAL( \
+ package_name, class_name, method_name)
// The indirection is needed to correctly expand the TC_PACKAGE_NAME macro.
+// See the explanation near ADD_QUOTES macro.
#define JNI_METHOD2(return_type, package_name, class_name, method_name) \
JNI_METHOD_PRIMITIVE(return_type, package_name, class_name, method_name)
#define JNI_METHOD(return_type, class_name, method_name) \
JNI_METHOD2(return_type, TC_PACKAGE_NAME, class_name, method_name)
+#define JNI_METHOD_NAME2(package_name, class_name, method_name) \
+ JNI_METHOD_NAME_INTERNAL(package_name, class_name, method_name)
+
+#define JNI_METHOD_NAME(class_name, method_name) \
+ JNI_METHOD_NAME2(TC_PACKAGE_NAME, class_name, method_name)
+
#ifdef __cplusplus
extern "C" {
#endif
// SmartSelection.
-JNI_METHOD(jlong, SmartSelection, nativeNew)
+JNI_METHOD(jlong, TC_CLASS_NAME, nativeNew)
(JNIEnv* env, jobject thiz, jint fd);
-JNI_METHOD(jlong, SmartSelection, nativeNewFromPath)
+JNI_METHOD(jlong, TC_CLASS_NAME, nativeNewFromPath)
(JNIEnv* env, jobject thiz, jstring path);
-JNI_METHOD(jlong, SmartSelection, nativeNewFromAssetFileDescriptor)
+JNI_METHOD(jlong, TC_CLASS_NAME, nativeNewFromAssetFileDescriptor)
(JNIEnv* env, jobject thiz, jobject afd, jlong offset, jlong size);
-JNI_METHOD(jintArray, SmartSelection, nativeSuggest)
+JNI_METHOD(jintArray, TC_CLASS_NAME, nativeSuggestSelection)
(JNIEnv* env, jobject thiz, jlong ptr, jstring context, jint selection_begin,
- jint selection_end);
+ jint selection_end, jobject options);
-JNI_METHOD(jobjectArray, SmartSelection, nativeClassifyText)
+JNI_METHOD(jobjectArray, TC_CLASS_NAME, nativeClassifyText)
(JNIEnv* env, jobject thiz, jlong ptr, jstring context, jint selection_begin,
- jint selection_end, jint input_flags);
+ jint selection_end, jobject options);
-JNI_METHOD(jobjectArray, SmartSelection, nativeAnnotate)
-(JNIEnv* env, jobject thiz, jlong ptr, jstring context);
+JNI_METHOD(jobjectArray, TC_CLASS_NAME, nativeAnnotate)
+(JNIEnv* env, jobject thiz, jlong ptr, jstring context, jobject options);
-JNI_METHOD(void, SmartSelection, nativeClose)
+JNI_METHOD(void, TC_CLASS_NAME, nativeClose)
(JNIEnv* env, jobject thiz, jlong ptr);
-JNI_METHOD(jstring, SmartSelection, nativeGetLanguage)
+// DEPRECATED. Use nativeGetLocales instead.
+JNI_METHOD(jstring, TC_CLASS_NAME, nativeGetLanguage)
(JNIEnv* env, jobject clazz, jint fd);
-JNI_METHOD(jint, SmartSelection, nativeGetVersion)
+JNI_METHOD(jstring, TC_CLASS_NAME, nativeGetLocales)
(JNIEnv* env, jobject clazz, jint fd);
-#ifndef LIBTEXTCLASSIFIER_DISABLE_LANG_ID
-// LangId.
-JNI_METHOD(jlong, LangId, nativeNew)(JNIEnv* env, jobject thiz, jint fd);
+JNI_METHOD(jstring, TC_CLASS_NAME, nativeGetLocalesFromAssetFileDescriptor)
+(JNIEnv* env, jobject thiz, jobject afd, jlong offset, jlong size);
-JNI_METHOD(jobjectArray, LangId, nativeFindLanguages)
-(JNIEnv* env, jobject thiz, jlong ptr, jstring text);
+JNI_METHOD(jint, TC_CLASS_NAME, nativeGetVersion)
+(JNIEnv* env, jobject clazz, jint fd);
-JNI_METHOD(void, LangId, nativeClose)(JNIEnv* env, jobject thiz, jlong ptr);
+JNI_METHOD(jint, TC_CLASS_NAME, nativeGetVersionFromAssetFileDescriptor)
+(JNIEnv* env, jobject thiz, jobject afd, jlong offset, jlong size);
-JNI_METHOD(int, LangId, nativeGetVersion)(JNIEnv* env, jobject clazz, jint fd);
-#endif
+JNI_METHOD(jstring, TC_CLASS_NAME, nativeGetName)
+(JNIEnv* env, jobject clazz, jint fd);
+
+JNI_METHOD(jstring, TC_CLASS_NAME, nativeGetNameFromAssetFileDescriptor)
+(JNIEnv* env, jobject thiz, jobject afd, jlong offset, jlong size);
#ifdef __cplusplus
}
#endif
-namespace libtextclassifier {
+namespace libtextclassifier2 {
// Given a utf8 string and a span expressed in Java BMP (basic multilingual
// plane) codepoints, converts it to a span expressed in utf8 codepoints.
-libtextclassifier::CodepointSpan ConvertIndicesBMPToUTF8(
- const std::string& utf8_str, libtextclassifier::CodepointSpan bmp_indices);
+libtextclassifier2::CodepointSpan ConvertIndicesBMPToUTF8(
+ const std::string& utf8_str, libtextclassifier2::CodepointSpan bmp_indices);
// Given a utf8 string and a span expressed in utf8 codepoints, converts it to a
// span expressed in Java BMP (basic multilingual plane) codepoints.
-libtextclassifier::CodepointSpan ConvertIndicesUTF8ToBMP(
- const std::string& utf8_str, libtextclassifier::CodepointSpan utf8_indices);
+libtextclassifier2::CodepointSpan ConvertIndicesUTF8ToBMP(
+ const std::string& utf8_str,
+ libtextclassifier2::CodepointSpan utf8_indices);
-} // namespace libtextclassifier
+} // namespace libtextclassifier2
#endif // LIBTEXTCLASSIFIER_TEXTCLASSIFIER_JNI_H_
diff --git a/textclassifier_jni_test.cc b/textclassifier_jni_test.cc
index ffc193b..87b96fa 100644
--- a/textclassifier_jni_test.cc
+++ b/textclassifier_jni_test.cc
@@ -19,7 +19,7 @@
#include "gmock/gmock.h"
#include "gtest/gtest.h"
-namespace libtextclassifier {
+namespace libtextclassifier2 {
namespace {
TEST(TextClassifier, ConvertIndicesBMPUTF8) {
@@ -76,4 +76,4 @@
}
} // namespace
-} // namespace libtextclassifier
+} // namespace libtextclassifier2
diff --git a/smartselect/token-feature-extractor.cc b/token-feature-extractor.cc
similarity index 70%
rename from smartselect/token-feature-extractor.cc
rename to token-feature-extractor.cc
index 6afd951..13fba30 100644
--- a/smartselect/token-feature-extractor.cc
+++ b/token-feature-extractor.cc
@@ -14,7 +14,7 @@
* limitations under the License.
*/
-#include "smartselect/token-feature-extractor.h"
+#include "token-feature-extractor.h"
#include <cctype>
#include <string>
@@ -23,12 +23,8 @@
#include "util/hash/farmhash.h"
#include "util/strings/stringpiece.h"
#include "util/utf8/unicodetext.h"
-#ifndef LIBTEXTCLASSIFIER_DISABLE_ICU_SUPPORT
-#include "unicode/regex.h"
-#include "unicode/uchar.h"
-#endif
-namespace libtextclassifier {
+namespace libtextclassifier2 {
namespace {
@@ -50,69 +46,121 @@
return copy;
}
-#ifndef LIBTEXTCLASSIFIER_DISABLE_ICU_SUPPORT
void RemapTokenUnicode(const std::string& token,
const TokenFeatureExtractorOptions& options,
- UnicodeText* remapped) {
+ const UniLib& unilib, UnicodeText* remapped) {
if (!options.remap_digits && !options.lowercase_tokens) {
// Leave remapped untouched.
return;
}
UnicodeText word = UTF8ToUnicodeText(token, /*do_copy=*/false);
- icu::UnicodeString icu_string;
+ remapped->clear();
for (auto it = word.begin(); it != word.end(); ++it) {
- if (options.remap_digits && u_isdigit(*it)) {
- icu_string.append('0');
+ if (options.remap_digits && unilib.IsDigit(*it)) {
+ remapped->AppendCodepoint('0');
} else if (options.lowercase_tokens) {
- icu_string.append(u_tolower(*it));
+ remapped->AppendCodepoint(unilib.ToLower(*it));
} else {
- icu_string.append(*it);
+ remapped->AppendCodepoint(*it);
}
}
- std::string utf8_str;
- icu_string.toUTF8String(utf8_str);
- remapped->CopyUTF8(utf8_str.data(), utf8_str.length());
}
-#endif
} // namespace
TokenFeatureExtractor::TokenFeatureExtractor(
- const TokenFeatureExtractorOptions& options)
- : options_(options) {
-#ifndef LIBTEXTCLASSIFIER_DISABLE_ICU_SUPPORT
- UErrorCode status;
+ const TokenFeatureExtractorOptions& options, const UniLib& unilib)
+ : options_(options), unilib_(unilib) {
for (const std::string& pattern : options.regexp_features) {
- status = U_ZERO_ERROR;
- regex_patterns_.push_back(
- std::unique_ptr<icu::RegexPattern>(icu::RegexPattern::compile(
- icu::UnicodeString(pattern.c_str(), pattern.size(), "utf-8"), 0,
- status)));
- if (U_FAILURE(status)) {
- TC_LOG(WARNING) << "Failed to load pattern" << pattern;
- }
+ regex_patterns_.push_back(std::unique_ptr<UniLib::RegexPattern>(
+ unilib_.CreateRegexPattern(UTF8ToUnicodeText(
+ pattern.c_str(), pattern.size(), /*do_copy=*/false))));
}
-#else
- bool found_unsupported_regexp_features = false;
- for (const std::string& pattern : options.regexp_features) {
- // A temporary solution to support this specific regexp pattern without
- // adding too much binary size.
- if (pattern == "^[^a-z]*$") {
- enable_all_caps_feature_ = true;
+}
+
+bool TokenFeatureExtractor::Extract(const Token& token, bool is_in_span,
+ std::vector<int>* sparse_features,
+ std::vector<float>* dense_features) const {
+ if (!dense_features) {
+ return false;
+ }
+ if (sparse_features) {
+ *sparse_features = ExtractCharactergramFeatures(token);
+ }
+ *dense_features = ExtractDenseFeatures(token, is_in_span);
+ return true;
+}
+
+std::vector<int> TokenFeatureExtractor::ExtractCharactergramFeatures(
+ const Token& token) const {
+ if (options_.unicode_aware_features) {
+ return ExtractCharactergramFeaturesUnicode(token);
+ } else {
+ return ExtractCharactergramFeaturesAscii(token);
+ }
+}
+
+std::vector<float> TokenFeatureExtractor::ExtractDenseFeatures(
+ const Token& token, bool is_in_span) const {
+ std::vector<float> dense_features;
+
+ if (options_.extract_case_feature) {
+ if (options_.unicode_aware_features) {
+ UnicodeText token_unicode =
+ UTF8ToUnicodeText(token.value, /*do_copy=*/false);
+ const bool is_upper = unilib_.IsUpper(*token_unicode.begin());
+ if (!token.value.empty() && is_upper) {
+ dense_features.push_back(1.0);
+ } else {
+ dense_features.push_back(-1.0);
+ }
} else {
- found_unsupported_regexp_features = true;
+ if (!token.value.empty() && isupper(*token.value.begin())) {
+ dense_features.push_back(1.0);
+ } else {
+ dense_features.push_back(-1.0);
+ }
}
}
- if (found_unsupported_regexp_features) {
- TC_LOG(WARNING) << "ICU not supported regexp features ignored.";
+
+ if (options_.extract_selection_mask_feature) {
+ if (is_in_span) {
+ dense_features.push_back(1.0);
+ } else {
+ if (options_.unicode_aware_features) {
+ dense_features.push_back(-1.0);
+ } else {
+ dense_features.push_back(0.0);
+ }
+ }
}
-#endif
+
+ // Add regexp features.
+ if (!regex_patterns_.empty()) {
+ UnicodeText token_unicode =
+ UTF8ToUnicodeText(token.value, /*do_copy=*/false);
+ for (int i = 0; i < regex_patterns_.size(); ++i) {
+ if (!regex_patterns_[i].get()) {
+ dense_features.push_back(-1.0);
+ continue;
+ }
+ auto matcher = regex_patterns_[i]->Matcher(token_unicode);
+ int status;
+ if (matcher->Matches(&status)) {
+ dense_features.push_back(1.0);
+ } else {
+ dense_features.push_back(-1.0);
+ }
+ }
+ }
+
+ return dense_features;
}
int TokenFeatureExtractor::HashToken(StringPiece token) const {
if (options_.allowed_chargrams.empty()) {
- return tcfarmhash::Fingerprint64(token) % options_.num_buckets;
+ return tc2farmhash::Fingerprint64(token) % options_.num_buckets;
} else {
// Padding and out-of-vocabulary tokens have extra buckets reserved because
// they are special and important tokens, and we don't want them to share
@@ -126,22 +174,13 @@
options_.allowed_chargrams.end()) {
return 0; // Out-of-vocabulary.
} else {
- return (tcfarmhash::Fingerprint64(token) %
+ return (tc2farmhash::Fingerprint64(token) %
(options_.num_buckets - kNumExtraBuckets)) +
kNumExtraBuckets;
}
}
}
-std::vector<int> TokenFeatureExtractor::ExtractCharactergramFeatures(
- const Token& token) const {
- if (options_.unicode_aware_features) {
- return ExtractCharactergramFeaturesUnicode(token);
- } else {
- return ExtractCharactergramFeaturesAscii(token);
- }
-}
-
std::vector<int> TokenFeatureExtractor::ExtractCharactergramFeaturesAscii(
const Token& token) const {
std::vector<int> result;
@@ -192,13 +231,12 @@
std::vector<int> TokenFeatureExtractor::ExtractCharactergramFeaturesUnicode(
const Token& token) const {
-#ifndef LIBTEXTCLASSIFIER_DISABLE_ICU_SUPPORT
std::vector<int> result;
if (token.is_padding || token.value.empty()) {
result.push_back(HashToken("<PAD>"));
} else {
UnicodeText word = UTF8ToUnicodeText(token.value, /*do_copy=*/false);
- RemapTokenUnicode(token.value, options_, &word);
+ RemapTokenUnicode(token.value, options_, unilib_, &word);
// Trim the word if needed by finding a left-cut point and right-cut point.
auto left_cut = word.begin();
@@ -268,98 +306,6 @@
}
}
return result;
-#else
- TC_LOG(WARNING) << "ICU not supported. No feature extracted.";
- return {};
-#endif
}
-bool TokenFeatureExtractor::Extract(const Token& token, bool is_in_span,
- std::vector<int>* sparse_features,
- std::vector<float>* dense_features) const {
- if (sparse_features == nullptr || dense_features == nullptr) {
- return false;
- }
-
- *sparse_features = ExtractCharactergramFeatures(token);
-
- if (options_.extract_case_feature) {
- if (options_.unicode_aware_features) {
- UnicodeText token_unicode =
- UTF8ToUnicodeText(token.value, /*do_copy=*/false);
- bool is_upper;
-#ifndef LIBTEXTCLASSIFIER_DISABLE_ICU_SUPPORT
- is_upper = u_isupper(*token_unicode.begin());
-#else
- TC_LOG(WARNING) << "Using non-unicode isupper because ICU is disabled.";
- is_upper = isupper(*token_unicode.begin());
-#endif
- if (!token.value.empty() && is_upper) {
- dense_features->push_back(1.0);
- } else {
- dense_features->push_back(-1.0);
- }
- } else {
- if (!token.value.empty() && isupper(*token.value.begin())) {
- dense_features->push_back(1.0);
- } else {
- dense_features->push_back(-1.0);
- }
- }
- }
-
- if (options_.extract_selection_mask_feature) {
- if (is_in_span) {
- dense_features->push_back(1.0);
- } else {
- if (options_.unicode_aware_features) {
- dense_features->push_back(-1.0);
- } else {
- dense_features->push_back(0.0);
- }
- }
- }
-
-#ifndef LIBTEXTCLASSIFIER_DISABLE_ICU_SUPPORT
- // Add regexp features.
- if (!regex_patterns_.empty()) {
- icu::UnicodeString unicode_str(token.value.c_str(), token.value.size(),
- "utf-8");
- for (int i = 0; i < regex_patterns_.size(); ++i) {
- if (!regex_patterns_[i].get()) {
- dense_features->push_back(-1.0);
- continue;
- }
-
- // Check for match.
- UErrorCode status = U_ZERO_ERROR;
- std::unique_ptr<icu::RegexMatcher> matcher(
- regex_patterns_[i]->matcher(unicode_str, status));
- if (matcher->find()) {
- dense_features->push_back(1.0);
- } else {
- dense_features->push_back(-1.0);
- }
- }
- }
-#else
- if (enable_all_caps_feature_) {
- bool is_all_caps = true;
- for (const char character_byte : token.value) {
- if (islower(character_byte)) {
- is_all_caps = false;
- break;
- }
- }
- if (is_all_caps) {
- dense_features->push_back(1.0);
- } else {
- dense_features->push_back(-1.0);
- }
- }
-#endif
-
- return true;
-}
-
-} // namespace libtextclassifier
+} // namespace libtextclassifier2
diff --git a/smartselect/token-feature-extractor.h b/token-feature-extractor.h
similarity index 69%
rename from smartselect/token-feature-extractor.h
rename to token-feature-extractor.h
index 5afeca4..fee1355 100644
--- a/smartselect/token-feature-extractor.h
+++ b/token-feature-extractor.h
@@ -14,20 +14,18 @@
* limitations under the License.
*/
-#ifndef LIBTEXTCLASSIFIER_SMARTSELECT_TOKEN_FEATURE_EXTRACTOR_H_
-#define LIBTEXTCLASSIFIER_SMARTSELECT_TOKEN_FEATURE_EXTRACTOR_H_
+#ifndef LIBTEXTCLASSIFIER_TOKEN_FEATURE_EXTRACTOR_H_
+#define LIBTEXTCLASSIFIER_TOKEN_FEATURE_EXTRACTOR_H_
#include <memory>
#include <unordered_set>
#include <vector>
-#include "smartselect/types.h"
+#include "types.h"
#include "util/strings/stringpiece.h"
-#ifndef LIBTEXTCLASSIFIER_DISABLE_ICU_SUPPORT
-#include "unicode/regex.h"
-#endif
+#include "util/utf8/unilib.h"
-namespace libtextclassifier {
+namespace libtextclassifier2 {
struct TokenFeatureExtractorOptions {
// Number of buckets used for hashing charactergrams.
@@ -67,29 +65,30 @@
class TokenFeatureExtractor {
public:
- explicit TokenFeatureExtractor(const TokenFeatureExtractorOptions& options);
+ TokenFeatureExtractor(const TokenFeatureExtractorOptions& options,
+ const UniLib& unilib);
- // Extracts features from a token.
- // - is_in_span is a bool indicator whether the token is a part of the
- // selection span (true) or not (false).
- // - sparse_features are indices into a sparse feature vector of size
- // options.num_buckets which are set to 1.0 (others are implicitly 0.0).
- // - dense_features are values of a dense feature vector of size 0-2
- // (depending on the options) for the token
+ // Extracts both the sparse (charactergram) and the dense features from a
+ // token. is_in_span is a bool indicator whether the token is a part of the
+ // selection span (true) or not (false).
+ // The sparse_features output is optional. Fails and returns false if
+ // dense_fatures in a nullptr.
bool Extract(const Token& token, bool is_in_span,
std::vector<int>* sparse_features,
std::vector<float>* dense_features) const;
+ // Extracts the sparse (charactergram) features from the token.
+ std::vector<int> ExtractCharactergramFeatures(const Token& token) const;
+
+ // Extracts the dense features from the token. is_in_span is a bool indicator
+ // whether the token is a part of the selection span (true) or not (false).
+ std::vector<float> ExtractDenseFeatures(const Token& token,
+ bool is_in_span) const;
+
int DenseFeaturesCount() const {
int feature_count =
options_.extract_case_feature + options_.extract_selection_mask_feature;
-#ifndef LIBTEXTCLASSIFIER_DISABLE_ICU_SUPPORT
feature_count += regex_patterns_.size();
-#else
- if (enable_all_caps_feature_) {
- feature_count += 1;
- }
-#endif
return feature_count;
}
@@ -97,9 +96,6 @@
// Hashes given token to given number of buckets.
int HashToken(StringPiece token) const;
- // Extracts the charactergram features from the token.
- std::vector<int> ExtractCharactergramFeatures(const Token& token) const;
-
// Extracts the charactergram features from the token in a non-unicode-aware
// way.
std::vector<int> ExtractCharactergramFeaturesAscii(const Token& token) const;
@@ -110,13 +106,10 @@
private:
TokenFeatureExtractorOptions options_;
-#ifndef LIBTEXTCLASSIFIER_DISABLE_ICU_SUPPORT
- std::vector<std::unique_ptr<icu::RegexPattern>> regex_patterns_;
-#else
- bool enable_all_caps_feature_ = false;
-#endif
+ std::vector<std::unique_ptr<UniLib::RegexPattern>> regex_patterns_;
+ const UniLib& unilib_;
};
-} // namespace libtextclassifier
+} // namespace libtextclassifier2
-#endif // LIBTEXTCLASSIFIER_SMARTSELECT_TOKEN_FEATURE_EXTRACTOR_H_
+#endif // LIBTEXTCLASSIFIER_TOKEN_FEATURE_EXTRACTOR_H_
diff --git a/smartselect/token-feature-extractor_test.cc b/token-feature-extractor_test.cc
similarity index 92%
rename from smartselect/token-feature-extractor_test.cc
rename to token-feature-extractor_test.cc
index 4b635fd..4b7e011 100644
--- a/smartselect/token-feature-extractor_test.cc
+++ b/token-feature-extractor_test.cc
@@ -14,18 +14,18 @@
* limitations under the License.
*/
-#include "smartselect/token-feature-extractor.h"
+#include "token-feature-extractor.h"
#include "gmock/gmock.h"
#include "gtest/gtest.h"
-namespace libtextclassifier {
+namespace libtextclassifier2 {
namespace {
class TestingTokenFeatureExtractor : public TokenFeatureExtractor {
public:
- using TokenFeatureExtractor::TokenFeatureExtractor;
using TokenFeatureExtractor::HashToken;
+ using TokenFeatureExtractor::TokenFeatureExtractor;
};
TEST(TokenFeatureExtractorTest, ExtractAscii) {
@@ -35,7 +35,8 @@
options.extract_case_feature = true;
options.unicode_aware_features = false;
options.extract_selection_mask_feature = true;
- TestingTokenFeatureExtractor extractor(options);
+ CREATE_UNILIB_FOR_TESTING
+ TestingTokenFeatureExtractor extractor(options, unilib);
std::vector<int> sparse_features;
std::vector<float> dense_features;
@@ -105,7 +106,8 @@
options.extract_case_feature = true;
options.unicode_aware_features = false;
options.extract_selection_mask_feature = true;
- TestingTokenFeatureExtractor extractor(options);
+ CREATE_UNILIB_FOR_TESTING
+ TestingTokenFeatureExtractor extractor(options, unilib);
std::vector<int> sparse_features;
std::vector<float> dense_features;
@@ -134,7 +136,8 @@
options.extract_case_feature = true;
options.unicode_aware_features = true;
options.extract_selection_mask_feature = true;
- TestingTokenFeatureExtractor extractor(options);
+ CREATE_UNILIB_FOR_TESTING
+ TestingTokenFeatureExtractor extractor(options, unilib);
std::vector<int> sparse_features;
std::vector<float> dense_features;
@@ -204,7 +207,8 @@
options.extract_case_feature = true;
options.unicode_aware_features = true;
options.extract_selection_mask_feature = true;
- TestingTokenFeatureExtractor extractor(options);
+ CREATE_UNILIB_FOR_TESTING
+ TestingTokenFeatureExtractor extractor(options, unilib);
std::vector<int> sparse_features;
std::vector<float> dense_features;
@@ -227,6 +231,7 @@
EXPECT_THAT(dense_features, testing::ElementsAreArray({-1.0, -1.0}));
}
+#ifdef LIBTEXTCLASSIFIER_TEST_ICU
TEST(TokenFeatureExtractorTest, ICUCaseFeature) {
TokenFeatureExtractorOptions options;
options.num_buckets = 1000;
@@ -234,7 +239,8 @@
options.extract_case_feature = true;
options.unicode_aware_features = true;
options.extract_selection_mask_feature = false;
- TokenFeatureExtractor extractor(options);
+ CREATE_UNILIB_FOR_TESTING
+ TestingTokenFeatureExtractor extractor(options, unilib);
std::vector<int> sparse_features;
std::vector<float> dense_features;
@@ -260,6 +266,7 @@
&dense_features);
EXPECT_THAT(dense_features, testing::ElementsAreArray({-1.0}));
}
+#endif
TEST(TokenFeatureExtractorTest, DigitRemapping) {
TokenFeatureExtractorOptions options;
@@ -267,7 +274,8 @@
options.chargram_orders = std::vector<int>{1, 2};
options.remap_digits = true;
options.unicode_aware_features = false;
- TokenFeatureExtractor extractor(options);
+ CREATE_UNILIB_FOR_TESTING
+ TestingTokenFeatureExtractor extractor(options, unilib);
std::vector<int> sparse_features;
std::vector<float> dense_features;
@@ -291,7 +299,8 @@
options.chargram_orders = std::vector<int>{1, 2};
options.remap_digits = true;
options.unicode_aware_features = true;
- TokenFeatureExtractor extractor(options);
+ CREATE_UNILIB_FOR_TESTING
+ TestingTokenFeatureExtractor extractor(options, unilib);
std::vector<int> sparse_features;
std::vector<float> dense_features;
@@ -315,7 +324,8 @@
options.chargram_orders = std::vector<int>{1, 2};
options.lowercase_tokens = true;
options.unicode_aware_features = false;
- TokenFeatureExtractor extractor(options);
+ CREATE_UNILIB_FOR_TESTING
+ TestingTokenFeatureExtractor extractor(options, unilib);
std::vector<int> sparse_features;
std::vector<float> dense_features;
@@ -332,13 +342,15 @@
EXPECT_THAT(sparse_features, testing::ElementsAreArray(sparse_features2));
}
+#ifdef LIBTEXTCLASSIFIER_TEST_ICU
TEST(TokenFeatureExtractorTest, LowercaseUnicode) {
TokenFeatureExtractorOptions options;
options.num_buckets = 1000;
options.chargram_orders = std::vector<int>{1, 2};
options.lowercase_tokens = true;
options.unicode_aware_features = true;
- TokenFeatureExtractor extractor(options);
+ CREATE_UNILIB_FOR_TESTING
+ TestingTokenFeatureExtractor extractor(options, unilib);
std::vector<int> sparse_features;
std::vector<float> dense_features;
@@ -349,7 +361,9 @@
&dense_features);
EXPECT_THAT(sparse_features, testing::ElementsAreArray(sparse_features2));
}
+#endif
+#ifdef LIBTEXTCLASSIFIER_TEST_ICU
TEST(TokenFeatureExtractorTest, RegexFeatures) {
TokenFeatureExtractorOptions options;
options.num_buckets = 1000;
@@ -358,7 +372,8 @@
options.unicode_aware_features = false;
options.regexp_features.push_back("^[a-z]+$"); // all lower case.
options.regexp_features.push_back("^[0-9]+$"); // all digits.
- TokenFeatureExtractor extractor(options);
+ CREATE_UNILIB_FOR_TESTING
+ TestingTokenFeatureExtractor extractor(options, unilib);
std::vector<int> sparse_features;
std::vector<float> dense_features;
@@ -381,6 +396,7 @@
&dense_features);
EXPECT_THAT(dense_features, testing::ElementsAreArray({-1.0, 1.0}));
}
+#endif
TEST(TokenFeatureExtractorTest, ExtractTooLongWord) {
TokenFeatureExtractorOptions options;
@@ -389,7 +405,8 @@
options.extract_case_feature = true;
options.unicode_aware_features = true;
options.extract_selection_mask_feature = true;
- TestingTokenFeatureExtractor extractor(options);
+ CREATE_UNILIB_FOR_TESTING
+ TestingTokenFeatureExtractor extractor(options, unilib);
// Test that this runs. ASAN should catch problems.
std::vector<int> sparse_features;
@@ -413,10 +430,12 @@
options.extract_case_feature = true;
options.unicode_aware_features = true;
options.extract_selection_mask_feature = true;
- TestingTokenFeatureExtractor extractor_unicode(options);
+
+ CREATE_UNILIB_FOR_TESTING
+ TestingTokenFeatureExtractor extractor_unicode(options, unilib);
options.unicode_aware_features = false;
- TestingTokenFeatureExtractor extractor_ascii(options);
+ TestingTokenFeatureExtractor extractor_ascii(options, unilib);
for (const std::string& input :
{"https://www.abcdefgh.com/in/xxxkkkvayio",
@@ -447,7 +466,8 @@
options.unicode_aware_features = false;
options.extract_selection_mask_feature = true;
- TestingTokenFeatureExtractor extractor(options);
+ CREATE_UNILIB_FOR_TESTING
+ TestingTokenFeatureExtractor extractor(options, unilib);
std::vector<int> sparse_features;
std::vector<float> dense_features;
@@ -473,7 +493,8 @@
options.allowed_chargrams.insert("!");
options.allowed_chargrams.insert("\xc4"); // UTF8 control character.
- TestingTokenFeatureExtractor extractor(options);
+ CREATE_UNILIB_FOR_TESTING
+ TestingTokenFeatureExtractor extractor(options, unilib);
std::vector<int> sparse_features;
std::vector<float> dense_features;
@@ -540,4 +561,4 @@
}
} // namespace
-} // namespace libtextclassifier
+} // namespace libtextclassifier2
diff --git a/tokenizer.cc b/tokenizer.cc
new file mode 100644
index 0000000..722a67b
--- /dev/null
+++ b/tokenizer.cc
@@ -0,0 +1,126 @@
+/*
+ * Copyright (C) 2017 The Android Open Source Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "tokenizer.h"
+
+#include <algorithm>
+
+#include "util/base/logging.h"
+#include "util/strings/utf8.h"
+
+namespace libtextclassifier2 {
+
+Tokenizer::Tokenizer(
+ const std::vector<const TokenizationCodepointRange*>& codepoint_ranges,
+ bool split_on_script_change)
+ : split_on_script_change_(split_on_script_change) {
+ for (const TokenizationCodepointRange* range : codepoint_ranges) {
+ codepoint_ranges_.emplace_back(range->UnPack());
+ }
+
+ std::sort(codepoint_ranges_.begin(), codepoint_ranges_.end(),
+ [](const std::unique_ptr<const TokenizationCodepointRangeT>& a,
+ const std::unique_ptr<const TokenizationCodepointRangeT>& b) {
+ return a->start < b->start;
+ });
+}
+
+const TokenizationCodepointRangeT* Tokenizer::FindTokenizationRange(
+ int codepoint) const {
+ auto it = std::lower_bound(
+ codepoint_ranges_.begin(), codepoint_ranges_.end(), codepoint,
+ [](const std::unique_ptr<const TokenizationCodepointRangeT>& range,
+ int codepoint) {
+ // This function compares range with the codepoint for the purpose of
+ // finding the first greater or equal range. Because of the use of
+ // std::lower_bound it needs to return true when range < codepoint;
+ // the first time it will return false the lower bound is found and
+ // returned.
+ //
+ // It might seem weird that the condition is range.end <= codepoint
+ // here but when codepoint == range.end it means it's actually just
+ // outside of the range, thus the range is less than the codepoint.
+ return range->end <= codepoint;
+ });
+ if (it != codepoint_ranges_.end() && (*it)->start <= codepoint &&
+ (*it)->end > codepoint) {
+ return it->get();
+ } else {
+ return nullptr;
+ }
+}
+
+void Tokenizer::GetScriptAndRole(char32 codepoint,
+ TokenizationCodepointRange_::Role* role,
+ int* script) const {
+ const TokenizationCodepointRangeT* range = FindTokenizationRange(codepoint);
+ if (range) {
+ *role = range->role;
+ *script = range->script_id;
+ } else {
+ *role = TokenizationCodepointRange_::Role_DEFAULT_ROLE;
+ *script = kUnknownScript;
+ }
+}
+
+std::vector<Token> Tokenizer::Tokenize(const std::string& text) const {
+ UnicodeText text_unicode = UTF8ToUnicodeText(text, /*do_copy=*/false);
+ return Tokenize(text_unicode);
+}
+
+std::vector<Token> Tokenizer::Tokenize(const UnicodeText& text_unicode) const {
+ std::vector<Token> result;
+ Token new_token("", 0, 0);
+ int codepoint_index = 0;
+
+ int last_script = kInvalidScript;
+ for (auto it = text_unicode.begin(); it != text_unicode.end();
+ ++it, ++codepoint_index) {
+ TokenizationCodepointRange_::Role role;
+ int script;
+ GetScriptAndRole(*it, &role, &script);
+
+ if (role & TokenizationCodepointRange_::Role_SPLIT_BEFORE ||
+ (split_on_script_change_ && last_script != kInvalidScript &&
+ last_script != script)) {
+ if (!new_token.value.empty()) {
+ result.push_back(new_token);
+ }
+ new_token = Token("", codepoint_index, codepoint_index);
+ }
+ if (!(role & TokenizationCodepointRange_::Role_DISCARD_CODEPOINT)) {
+ new_token.value += std::string(
+ it.utf8_data(),
+ it.utf8_data() + GetNumBytesForNonZeroUTF8Char(it.utf8_data()));
+ ++new_token.end;
+ }
+ if (role & TokenizationCodepointRange_::Role_SPLIT_AFTER) {
+ if (!new_token.value.empty()) {
+ result.push_back(new_token);
+ }
+ new_token = Token("", codepoint_index + 1, codepoint_index + 1);
+ }
+
+ last_script = script;
+ }
+ if (!new_token.value.empty()) {
+ result.push_back(new_token);
+ }
+
+ return result;
+}
+
+} // namespace libtextclassifier2
diff --git a/tokenizer.h b/tokenizer.h
new file mode 100644
index 0000000..2524e12
--- /dev/null
+++ b/tokenizer.h
@@ -0,0 +1,71 @@
+/*
+ * Copyright (C) 2017 The Android Open Source Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#ifndef LIBTEXTCLASSIFIER_TOKENIZER_H_
+#define LIBTEXTCLASSIFIER_TOKENIZER_H_
+
+#include <string>
+#include <vector>
+
+#include "model_generated.h"
+#include "types.h"
+#include "util/base/integral_types.h"
+#include "util/utf8/unicodetext.h"
+
+namespace libtextclassifier2 {
+
+const int kInvalidScript = -1;
+const int kUnknownScript = -2;
+
+// Tokenizer splits the input string into a sequence of tokens, according to the
+// configuration.
+class Tokenizer {
+ public:
+ explicit Tokenizer(
+ const std::vector<const TokenizationCodepointRange*>& codepoint_ranges,
+ bool split_on_script_change);
+
+ // Tokenizes the input string using the selected tokenization method.
+ std::vector<Token> Tokenize(const std::string& text) const;
+
+ // Same as above but takes UnicodeText.
+ std::vector<Token> Tokenize(const UnicodeText& text_unicode) const;
+
+ protected:
+ // Finds the tokenization codepoint range config for given codepoint.
+ // Internally uses binary search so should be O(log(# of codepoint_ranges)).
+ const TokenizationCodepointRangeT* FindTokenizationRange(int codepoint) const;
+
+ // Finds the role and script for given codepoint. If not found, DEFAULT_ROLE
+ // and kUnknownScript are assigned.
+ void GetScriptAndRole(char32 codepoint,
+ TokenizationCodepointRange_::Role* role,
+ int* script) const;
+
+ private:
+ // Codepoint ranges that determine how different codepoints are tokenized.
+ // The ranges must not overlap.
+ std::vector<std::unique_ptr<const TokenizationCodepointRangeT>>
+ codepoint_ranges_;
+
+ // If true, tokens will be additionally split when the codepoint's script_id
+ // changes.
+ bool split_on_script_change_;
+};
+
+} // namespace libtextclassifier2
+
+#endif // LIBTEXTCLASSIFIER_TOKENIZER_H_
diff --git a/tokenizer_test.cc b/tokenizer_test.cc
new file mode 100644
index 0000000..65072f3
--- /dev/null
+++ b/tokenizer_test.cc
@@ -0,0 +1,334 @@
+/*
+ * Copyright (C) 2017 The Android Open Source Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "tokenizer.h"
+
+#include <vector>
+
+#include "gmock/gmock.h"
+#include "gtest/gtest.h"
+
+namespace libtextclassifier2 {
+namespace {
+
+using testing::ElementsAreArray;
+
+class TestingTokenizer : public Tokenizer {
+ public:
+ explicit TestingTokenizer(
+ const std::vector<const TokenizationCodepointRange*>&
+ codepoint_range_configs,
+ bool split_on_script_change)
+ : Tokenizer(codepoint_range_configs, split_on_script_change) {}
+
+ using Tokenizer::FindTokenizationRange;
+};
+
+class TestingTokenizerProxy {
+ public:
+ explicit TestingTokenizerProxy(
+ const std::vector<TokenizationCodepointRangeT>& codepoint_range_configs,
+ bool split_on_script_change) {
+ int num_configs = codepoint_range_configs.size();
+ std::vector<const TokenizationCodepointRange*> configs_fb;
+ buffers_.reserve(num_configs);
+ for (int i = 0; i < num_configs; i++) {
+ flatbuffers::FlatBufferBuilder builder;
+ builder.Finish(CreateTokenizationCodepointRange(
+ builder, &codepoint_range_configs[i]));
+ buffers_.push_back(builder.Release());
+ configs_fb.push_back(
+ flatbuffers::GetRoot<TokenizationCodepointRange>(buffers_[i].data()));
+ }
+ tokenizer_ = std::unique_ptr<TestingTokenizer>(
+ new TestingTokenizer(configs_fb, split_on_script_change));
+ }
+
+ TokenizationCodepointRange_::Role TestFindTokenizationRole(int c) const {
+ const TokenizationCodepointRangeT* range =
+ tokenizer_->FindTokenizationRange(c);
+ if (range != nullptr) {
+ return range->role;
+ } else {
+ return TokenizationCodepointRange_::Role_DEFAULT_ROLE;
+ }
+ }
+
+ std::vector<Token> Tokenize(const std::string& utf8_text) const {
+ return tokenizer_->Tokenize(utf8_text);
+ }
+
+ private:
+ std::vector<flatbuffers::DetachedBuffer> buffers_;
+ std::unique_ptr<TestingTokenizer> tokenizer_;
+};
+
+TEST(TokenizerTest, FindTokenizationRange) {
+ std::vector<TokenizationCodepointRangeT> configs;
+ TokenizationCodepointRangeT* config;
+
+ configs.emplace_back();
+ config = &configs.back();
+ config->start = 0;
+ config->end = 10;
+ config->role = TokenizationCodepointRange_::Role_TOKEN_SEPARATOR;
+
+ configs.emplace_back();
+ config = &configs.back();
+ config->start = 32;
+ config->end = 33;
+ config->role = TokenizationCodepointRange_::Role_WHITESPACE_SEPARATOR;
+
+ configs.emplace_back();
+ config = &configs.back();
+ config->start = 1234;
+ config->end = 12345;
+ config->role = TokenizationCodepointRange_::Role_TOKEN_SEPARATOR;
+
+ TestingTokenizerProxy tokenizer(configs, /*split_on_script_change=*/false);
+
+ // Test hits to the first group.
+ EXPECT_EQ(tokenizer.TestFindTokenizationRole(0),
+ TokenizationCodepointRange_::Role_TOKEN_SEPARATOR);
+ EXPECT_EQ(tokenizer.TestFindTokenizationRole(5),
+ TokenizationCodepointRange_::Role_TOKEN_SEPARATOR);
+ EXPECT_EQ(tokenizer.TestFindTokenizationRole(10),
+ TokenizationCodepointRange_::Role_DEFAULT_ROLE);
+
+ // Test a hit to the second group.
+ EXPECT_EQ(tokenizer.TestFindTokenizationRole(31),
+ TokenizationCodepointRange_::Role_DEFAULT_ROLE);
+ EXPECT_EQ(tokenizer.TestFindTokenizationRole(32),
+ TokenizationCodepointRange_::Role_WHITESPACE_SEPARATOR);
+ EXPECT_EQ(tokenizer.TestFindTokenizationRole(33),
+ TokenizationCodepointRange_::Role_DEFAULT_ROLE);
+
+ // Test hits to the third group.
+ EXPECT_EQ(tokenizer.TestFindTokenizationRole(1233),
+ TokenizationCodepointRange_::Role_DEFAULT_ROLE);
+ EXPECT_EQ(tokenizer.TestFindTokenizationRole(1234),
+ TokenizationCodepointRange_::Role_TOKEN_SEPARATOR);
+ EXPECT_EQ(tokenizer.TestFindTokenizationRole(12344),
+ TokenizationCodepointRange_::Role_TOKEN_SEPARATOR);
+ EXPECT_EQ(tokenizer.TestFindTokenizationRole(12345),
+ TokenizationCodepointRange_::Role_DEFAULT_ROLE);
+
+ // Test a hit outside.
+ EXPECT_EQ(tokenizer.TestFindTokenizationRole(99),
+ TokenizationCodepointRange_::Role_DEFAULT_ROLE);
+}
+
+TEST(TokenizerTest, TokenizeOnSpace) {
+ std::vector<TokenizationCodepointRangeT> configs;
+ TokenizationCodepointRangeT* config;
+
+ configs.emplace_back();
+ config = &configs.back();
+ // Space character.
+ config->start = 32;
+ config->end = 33;
+ config->role = TokenizationCodepointRange_::Role_WHITESPACE_SEPARATOR;
+
+ TestingTokenizerProxy tokenizer(configs, /*split_on_script_change=*/false);
+ std::vector<Token> tokens = tokenizer.Tokenize("Hello world!");
+
+ EXPECT_THAT(tokens,
+ ElementsAreArray({Token("Hello", 0, 5), Token("world!", 6, 12)}));
+}
+
+TEST(TokenizerTest, TokenizeOnSpaceAndScriptChange) {
+ std::vector<TokenizationCodepointRangeT> configs;
+ TokenizationCodepointRangeT* config;
+
+ // Latin.
+ configs.emplace_back();
+ config = &configs.back();
+ config->start = 0;
+ config->end = 32;
+ config->role = TokenizationCodepointRange_::Role_DEFAULT_ROLE;
+ config->script_id = 1;
+ configs.emplace_back();
+ config = &configs.back();
+ config->start = 32;
+ config->end = 33;
+ config->role = TokenizationCodepointRange_::Role_WHITESPACE_SEPARATOR;
+ config->script_id = 1;
+ configs.emplace_back();
+ config = &configs.back();
+ config->start = 33;
+ config->end = 0x77F + 1;
+ config->role = TokenizationCodepointRange_::Role_DEFAULT_ROLE;
+ config->script_id = 1;
+
+ TestingTokenizerProxy tokenizer(configs, /*split_on_script_change=*/true);
+ EXPECT_THAT(tokenizer.Tokenize("앨라배마 주 전화(123) 456-789웹사이트"),
+ std::vector<Token>({Token("앨라배마", 0, 4), Token("주", 5, 6),
+ Token("전화", 7, 10), Token("(123)", 10, 15),
+ Token("456-789", 16, 23),
+ Token("웹사이트", 23, 28)}));
+} // namespace
+
+TEST(TokenizerTest, TokenizeComplex) {
+ std::vector<TokenizationCodepointRangeT> configs;
+ TokenizationCodepointRangeT* config;
+
+ // Source: http://www.unicode.org/Public/10.0.0/ucd/Blocks-10.0.0d1.txt
+ // Latin - cyrilic.
+ // 0000..007F; Basic Latin
+ // 0080..00FF; Latin-1 Supplement
+ // 0100..017F; Latin Extended-A
+ // 0180..024F; Latin Extended-B
+ // 0250..02AF; IPA Extensions
+ // 02B0..02FF; Spacing Modifier Letters
+ // 0300..036F; Combining Diacritical Marks
+ // 0370..03FF; Greek and Coptic
+ // 0400..04FF; Cyrillic
+ // 0500..052F; Cyrillic Supplement
+ // 0530..058F; Armenian
+ // 0590..05FF; Hebrew
+ // 0600..06FF; Arabic
+ // 0700..074F; Syriac
+ // 0750..077F; Arabic Supplement
+ configs.emplace_back();
+ config = &configs.back();
+ config->start = 0;
+ config->end = 32;
+ config->role = TokenizationCodepointRange_::Role_DEFAULT_ROLE;
+ configs.emplace_back();
+ config = &configs.back();
+ config->start = 32;
+ config->end = 33;
+ config->role = TokenizationCodepointRange_::Role_WHITESPACE_SEPARATOR;
+ configs.emplace_back();
+ config = &configs.back();
+ config->start = 33;
+ config->end = 0x77F + 1;
+ config->role = TokenizationCodepointRange_::Role_DEFAULT_ROLE;
+
+ // CJK
+ // 2E80..2EFF; CJK Radicals Supplement
+ // 3000..303F; CJK Symbols and Punctuation
+ // 3040..309F; Hiragana
+ // 30A0..30FF; Katakana
+ // 3100..312F; Bopomofo
+ // 3130..318F; Hangul Compatibility Jamo
+ // 3190..319F; Kanbun
+ // 31A0..31BF; Bopomofo Extended
+ // 31C0..31EF; CJK Strokes
+ // 31F0..31FF; Katakana Phonetic Extensions
+ // 3200..32FF; Enclosed CJK Letters and Months
+ // 3300..33FF; CJK Compatibility
+ // 3400..4DBF; CJK Unified Ideographs Extension A
+ // 4DC0..4DFF; Yijing Hexagram Symbols
+ // 4E00..9FFF; CJK Unified Ideographs
+ // A000..A48F; Yi Syllables
+ // A490..A4CF; Yi Radicals
+ // A4D0..A4FF; Lisu
+ // A500..A63F; Vai
+ // F900..FAFF; CJK Compatibility Ideographs
+ // FE30..FE4F; CJK Compatibility Forms
+ // 20000..2A6DF; CJK Unified Ideographs Extension B
+ // 2A700..2B73F; CJK Unified Ideographs Extension C
+ // 2B740..2B81F; CJK Unified Ideographs Extension D
+ // 2B820..2CEAF; CJK Unified Ideographs Extension E
+ // 2CEB0..2EBEF; CJK Unified Ideographs Extension F
+ // 2F800..2FA1F; CJK Compatibility Ideographs Supplement
+ configs.emplace_back();
+ config = &configs.back();
+ config->start = 0x2E80;
+ config->end = 0x2EFF + 1;
+ config->role = TokenizationCodepointRange_::Role_TOKEN_SEPARATOR;
+ configs.emplace_back();
+ config = &configs.back();
+ config->start = 0x3000;
+ config->end = 0xA63F + 1;
+ config->role = TokenizationCodepointRange_::Role_TOKEN_SEPARATOR;
+ configs.emplace_back();
+ config = &configs.back();
+ config->start = 0xF900;
+ config->end = 0xFAFF + 1;
+ config->role = TokenizationCodepointRange_::Role_TOKEN_SEPARATOR;
+ configs.emplace_back();
+ config = &configs.back();
+ config->start = 0xFE30;
+ config->end = 0xFE4F + 1;
+ config->role = TokenizationCodepointRange_::Role_TOKEN_SEPARATOR;
+ configs.emplace_back();
+ config = &configs.back();
+ config->start = 0x20000;
+ config->end = 0x2A6DF + 1;
+ config->role = TokenizationCodepointRange_::Role_TOKEN_SEPARATOR;
+ configs.emplace_back();
+ config = &configs.back();
+ config->start = 0x2A700;
+ config->end = 0x2B73F + 1;
+ config->role = TokenizationCodepointRange_::Role_TOKEN_SEPARATOR;
+ configs.emplace_back();
+ config = &configs.back();
+ config->start = 0x2B740;
+ config->end = 0x2B81F + 1;
+ config->role = TokenizationCodepointRange_::Role_TOKEN_SEPARATOR;
+ configs.emplace_back();
+ config = &configs.back();
+ config->start = 0x2B820;
+ config->end = 0x2CEAF + 1;
+ config->role = TokenizationCodepointRange_::Role_TOKEN_SEPARATOR;
+ configs.emplace_back();
+ config = &configs.back();
+ config->start = 0x2CEB0;
+ config->end = 0x2EBEF + 1;
+ config->role = TokenizationCodepointRange_::Role_TOKEN_SEPARATOR;
+ configs.emplace_back();
+ config = &configs.back();
+ config->start = 0x2F800;
+ config->end = 0x2FA1F + 1;
+ config->role = TokenizationCodepointRange_::Role_TOKEN_SEPARATOR;
+
+ // Thai.
+ // 0E00..0E7F; Thai
+ configs.emplace_back();
+ config = &configs.back();
+ config->start = 0x0E00;
+ config->end = 0x0E7F + 1;
+ config->role = TokenizationCodepointRange_::Role_TOKEN_SEPARATOR;
+
+ TestingTokenizerProxy tokenizer(configs, /*split_on_script_change=*/false);
+ std::vector<Token> tokens;
+
+ tokens = tokenizer.Tokenize(
+ "問少目木輸走猶術権自京門録球変。細開括省用掲情結傍走愛明氷。");
+ EXPECT_EQ(tokens.size(), 30);
+
+ tokens = tokenizer.Tokenize("問少目 hello 木輸ยามきゃ");
+ // clang-format off
+ EXPECT_THAT(
+ tokens,
+ ElementsAreArray({Token("問", 0, 1),
+ Token("少", 1, 2),
+ Token("目", 2, 3),
+ Token("hello", 4, 9),
+ Token("木", 10, 11),
+ Token("輸", 11, 12),
+ Token("ย", 12, 13),
+ Token("า", 13, 14),
+ Token("ม", 14, 15),
+ Token("き", 15, 16),
+ Token("ゃ", 16, 17)}));
+ // clang-format on
+}
+
+} // namespace
+} // namespace libtextclassifier2
diff --git a/types-test-util.h b/types-test-util.h
new file mode 100644
index 0000000..1679e7c
--- /dev/null
+++ b/types-test-util.h
@@ -0,0 +1,49 @@
+/*
+ * Copyright (C) 2017 The Android Open Source Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#ifndef LIBTEXTCLASSIFIER_TYPES_TEST_UTIL_H_
+#define LIBTEXTCLASSIFIER_TYPES_TEST_UTIL_H_
+
+#include <ostream>
+
+#include "types.h"
+#include "util/base/logging.h"
+
+namespace libtextclassifier2 {
+
+inline std::ostream& operator<<(std::ostream& stream, const Token& value) {
+ logging::LoggingStringStream tmp_stream;
+ tmp_stream << value;
+ return stream << tmp_stream.message;
+}
+
+inline std::ostream& operator<<(std::ostream& stream,
+ const AnnotatedSpan& value) {
+ logging::LoggingStringStream tmp_stream;
+ tmp_stream << value;
+ return stream << tmp_stream.message;
+}
+
+inline std::ostream& operator<<(std::ostream& stream,
+ const DatetimeParseResultSpan& value) {
+ logging::LoggingStringStream tmp_stream;
+ tmp_stream << value;
+ return stream << tmp_stream.message;
+}
+
+} // namespace libtextclassifier2
+
+#endif // LIBTEXTCLASSIFIER_TYPES_TEST_UTIL_H_
diff --git a/types.h b/types.h
new file mode 100644
index 0000000..b2f624d
--- /dev/null
+++ b/types.h
@@ -0,0 +1,396 @@
+/*
+ * Copyright (C) 2017 The Android Open Source Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#ifndef LIBTEXTCLASSIFIER_TYPES_H_
+#define LIBTEXTCLASSIFIER_TYPES_H_
+
+#include <algorithm>
+#include <cmath>
+#include <functional>
+#include <set>
+#include <string>
+#include <utility>
+#include <vector>
+#include "util/base/integral_types.h"
+
+#include "util/base/logging.h"
+
+namespace libtextclassifier2 {
+
+constexpr int kInvalidIndex = -1;
+
+// Index for a 0-based array of tokens.
+using TokenIndex = int;
+
+// Index for a 0-based array of codepoints.
+using CodepointIndex = int;
+
+// Marks a span in a sequence of codepoints. The first element is the index of
+// the first codepoint of the span, and the second element is the index of the
+// codepoint one past the end of the span.
+// TODO(b/71982294): Make it a struct.
+using CodepointSpan = std::pair<CodepointIndex, CodepointIndex>;
+
+inline bool SpansOverlap(const CodepointSpan& a, const CodepointSpan& b) {
+ return a.first < b.second && b.first < a.second;
+}
+
+inline bool ValidNonEmptySpan(const CodepointSpan& span) {
+ return span.first < span.second && span.first >= 0 && span.second >= 0;
+}
+
+template <typename T>
+bool DoesCandidateConflict(
+ const int considered_candidate, const std::vector<T>& candidates,
+ const std::set<int, std::function<bool(int, int)>>& chosen_indices_set) {
+ if (chosen_indices_set.empty()) {
+ return false;
+ }
+
+ auto conflicting_it = chosen_indices_set.lower_bound(considered_candidate);
+ // Check conflict on the right.
+ if (conflicting_it != chosen_indices_set.end() &&
+ SpansOverlap(candidates[considered_candidate].span,
+ candidates[*conflicting_it].span)) {
+ return true;
+ }
+
+ // Check conflict on the left.
+ // If we can't go more left, there can't be a conflict:
+ if (conflicting_it == chosen_indices_set.begin()) {
+ return false;
+ }
+ // Otherwise move one span left and insert if it doesn't overlap with the
+ // candidate.
+ --conflicting_it;
+ if (!SpansOverlap(candidates[considered_candidate].span,
+ candidates[*conflicting_it].span)) {
+ return false;
+ }
+
+ return true;
+}
+
+// Marks a span in a sequence of tokens. The first element is the index of the
+// first token in the span, and the second element is the index of the token one
+// past the end of the span.
+// TODO(b/71982294): Make it a struct.
+using TokenSpan = std::pair<TokenIndex, TokenIndex>;
+
+// Returns the size of the token span. Assumes that the span is valid.
+inline int TokenSpanSize(const TokenSpan& token_span) {
+ return token_span.second - token_span.first;
+}
+
+// Returns a token span consisting of one token.
+inline TokenSpan SingleTokenSpan(int token_index) {
+ return {token_index, token_index + 1};
+}
+
+// Returns an intersection of two token spans. Assumes that both spans are valid
+// and overlapping.
+inline TokenSpan IntersectTokenSpans(const TokenSpan& token_span1,
+ const TokenSpan& token_span2) {
+ return {std::max(token_span1.first, token_span2.first),
+ std::min(token_span1.second, token_span2.second)};
+}
+
+// Returns and expanded token span by adding a certain number of tokens on its
+// left and on its right.
+inline TokenSpan ExpandTokenSpan(const TokenSpan& token_span,
+ int num_tokens_left, int num_tokens_right) {
+ return {token_span.first - num_tokens_left,
+ token_span.second + num_tokens_right};
+}
+
+// Token holds a token, its position in the original string and whether it was
+// part of the input span.
+struct Token {
+ std::string value;
+ CodepointIndex start;
+ CodepointIndex end;
+
+ // Whether the token is a padding token.
+ bool is_padding;
+
+ // Default constructor constructs the padding-token.
+ Token()
+ : value(""), start(kInvalidIndex), end(kInvalidIndex), is_padding(true) {}
+
+ Token(const std::string& arg_value, CodepointIndex arg_start,
+ CodepointIndex arg_end)
+ : value(arg_value), start(arg_start), end(arg_end), is_padding(false) {}
+
+ bool operator==(const Token& other) const {
+ return value == other.value && start == other.start && end == other.end &&
+ is_padding == other.is_padding;
+ }
+
+ bool IsContainedInSpan(CodepointSpan span) const {
+ return start >= span.first && end <= span.second;
+ }
+};
+
+// Pretty-printing function for Token.
+inline logging::LoggingStringStream& operator<<(
+ logging::LoggingStringStream& stream, const Token& token) {
+ if (!token.is_padding) {
+ return stream << "Token(\"" << token.value << "\", " << token.start << ", "
+ << token.end << ")";
+ } else {
+ return stream << "Token()";
+ }
+}
+
+enum DatetimeGranularity {
+ GRANULARITY_UNKNOWN = -1, // GRANULARITY_UNKNOWN is used as a proxy for this
+ // structure being uninitialized.
+ GRANULARITY_YEAR = 0,
+ GRANULARITY_MONTH = 1,
+ GRANULARITY_WEEK = 2,
+ GRANULARITY_DAY = 3,
+ GRANULARITY_HOUR = 4,
+ GRANULARITY_MINUTE = 5,
+ GRANULARITY_SECOND = 6
+};
+
+struct DatetimeParseResult {
+ // The absolute time in milliseconds since the epoch in UTC. This is derived
+ // from the reference time and the fields specified in the text - so it may
+ // be imperfect where the time was ambiguous. (e.g. "at 7:30" may be am or pm)
+ int64 time_ms_utc;
+
+ // The precision of the estimate then in to calculating the milliseconds
+ DatetimeGranularity granularity;
+
+ DatetimeParseResult() : time_ms_utc(0), granularity(GRANULARITY_UNKNOWN) {}
+
+ DatetimeParseResult(int64 arg_time_ms_utc,
+ DatetimeGranularity arg_granularity)
+ : time_ms_utc(arg_time_ms_utc), granularity(arg_granularity) {}
+
+ bool IsSet() const { return granularity != GRANULARITY_UNKNOWN; }
+
+ bool operator==(const DatetimeParseResult& other) const {
+ return granularity == other.granularity && time_ms_utc == other.time_ms_utc;
+ }
+};
+
+const float kFloatCompareEpsilon = 1e-5;
+
+struct DatetimeParseResultSpan {
+ CodepointSpan span;
+ DatetimeParseResult data;
+ float target_classification_score;
+ float priority_score;
+
+ bool operator==(const DatetimeParseResultSpan& other) const {
+ return span == other.span && data.granularity == other.data.granularity &&
+ data.time_ms_utc == other.data.time_ms_utc &&
+ std::abs(target_classification_score -
+ other.target_classification_score) < kFloatCompareEpsilon &&
+ std::abs(priority_score - other.priority_score) <
+ kFloatCompareEpsilon;
+ }
+};
+
+// Pretty-printing function for DatetimeParseResultSpan.
+inline logging::LoggingStringStream& operator<<(
+ logging::LoggingStringStream& stream,
+ const DatetimeParseResultSpan& value) {
+ return stream << "DatetimeParseResultSpan({" << value.span.first << ", "
+ << value.span.second << "}, {/*time_ms_utc=*/ "
+ << value.data.time_ms_utc << ", /*granularity=*/ "
+ << value.data.granularity << "})";
+}
+
+struct ClassificationResult {
+ std::string collection;
+ float score;
+ DatetimeParseResult datetime_parse_result;
+
+ // Internal score used for conflict resolution.
+ float priority_score;
+
+ explicit ClassificationResult() : score(-1.0f), priority_score(-1.0) {}
+
+ ClassificationResult(const std::string& arg_collection, float arg_score)
+ : collection(arg_collection),
+ score(arg_score),
+ priority_score(arg_score) {}
+
+ ClassificationResult(const std::string& arg_collection, float arg_score,
+ float arg_priority_score)
+ : collection(arg_collection),
+ score(arg_score),
+ priority_score(arg_priority_score) {}
+};
+
+// Pretty-printing function for ClassificationResult.
+inline logging::LoggingStringStream& operator<<(
+ logging::LoggingStringStream& stream, const ClassificationResult& result) {
+ return stream << "ClassificationResult(" << result.collection << ", "
+ << result.score << ")";
+}
+
+// Pretty-printing function for std::vector<ClassificationResult>.
+inline logging::LoggingStringStream& operator<<(
+ logging::LoggingStringStream& stream,
+ const std::vector<ClassificationResult>& results) {
+ stream = stream << "{\n";
+ for (const ClassificationResult& result : results) {
+ stream = stream << " " << result << "\n";
+ }
+ stream = stream << "}";
+ return stream;
+}
+
+// Represents a result of Annotate call.
+struct AnnotatedSpan {
+ // Unicode codepoint indices in the input string.
+ CodepointSpan span = {kInvalidIndex, kInvalidIndex};
+
+ // Classification result for the span.
+ std::vector<ClassificationResult> classification;
+};
+
+// Pretty-printing function for AnnotatedSpan.
+inline logging::LoggingStringStream& operator<<(
+ logging::LoggingStringStream& stream, const AnnotatedSpan& span) {
+ std::string best_class;
+ float best_score = -1;
+ if (!span.classification.empty()) {
+ best_class = span.classification[0].collection;
+ best_score = span.classification[0].score;
+ }
+ return stream << "Span(" << span.span.first << ", " << span.span.second
+ << ", " << best_class << ", " << best_score << ")";
+}
+
+// StringPiece analogue for std::vector<T>.
+template <class T>
+class VectorSpan {
+ public:
+ VectorSpan() : begin_(), end_() {}
+ VectorSpan(const std::vector<T>& v) // NOLINT(runtime/explicit)
+ : begin_(v.begin()), end_(v.end()) {}
+ VectorSpan(typename std::vector<T>::const_iterator begin,
+ typename std::vector<T>::const_iterator end)
+ : begin_(begin), end_(end) {}
+
+ const T& operator[](typename std::vector<T>::size_type i) const {
+ return *(begin_ + i);
+ }
+
+ int size() const { return end_ - begin_; }
+ typename std::vector<T>::const_iterator begin() const { return begin_; }
+ typename std::vector<T>::const_iterator end() const { return end_; }
+ const float* data() const { return &(*begin_); }
+
+ private:
+ typename std::vector<T>::const_iterator begin_;
+ typename std::vector<T>::const_iterator end_;
+};
+
+struct DateParseData {
+ enum Relation {
+ NEXT = 1,
+ NEXT_OR_SAME = 2,
+ LAST = 3,
+ NOW = 4,
+ TOMORROW = 5,
+ YESTERDAY = 6,
+ PAST = 7,
+ FUTURE = 8
+ };
+
+ enum RelationType {
+ MONDAY = 1,
+ TUESDAY = 2,
+ WEDNESDAY = 3,
+ THURSDAY = 4,
+ FRIDAY = 5,
+ SATURDAY = 6,
+ SUNDAY = 7,
+ DAY = 8,
+ WEEK = 9,
+ MONTH = 10,
+ YEAR = 11
+ };
+
+ enum Fields {
+ YEAR_FIELD = 1 << 0,
+ MONTH_FIELD = 1 << 1,
+ DAY_FIELD = 1 << 2,
+ HOUR_FIELD = 1 << 3,
+ MINUTE_FIELD = 1 << 4,
+ SECOND_FIELD = 1 << 5,
+ AMPM_FIELD = 1 << 6,
+ ZONE_OFFSET_FIELD = 1 << 7,
+ DST_OFFSET_FIELD = 1 << 8,
+ RELATION_FIELD = 1 << 9,
+ RELATION_TYPE_FIELD = 1 << 10,
+ RELATION_DISTANCE_FIELD = 1 << 11
+ };
+
+ enum AMPM { AM = 0, PM = 1 };
+
+ enum TimeUnit {
+ DAYS = 1,
+ WEEKS = 2,
+ MONTHS = 3,
+ HOURS = 4,
+ MINUTES = 5,
+ SECONDS = 6,
+ YEARS = 7
+ };
+
+ // Bit mask of fields which have been set on the struct
+ int field_set_mask;
+
+ // Fields describing absolute date fields.
+ // Year of the date seen in the text match.
+ int year;
+ // Month of the year starting with January = 1.
+ int month;
+ // Day of the month starting with 1.
+ int day_of_month;
+ // Hour of the day with a range of 0-23,
+ // values less than 12 need the AMPM field below or heuristics
+ // to definitively determine the time.
+ int hour;
+ // Hour of the day with a range of 0-59.
+ int minute;
+ // Hour of the day with a range of 0-59.
+ int second;
+ // 0 == AM, 1 == PM
+ int ampm;
+ // Number of hours offset from UTC this date time is in.
+ int zone_offset;
+ // Number of hours offest for DST
+ int dst_offset;
+
+ // The permutation from now that was made to find the date time.
+ Relation relation;
+ // The unit of measure of the change to the date time.
+ RelationType relation_type;
+ // The number of units of change that were made.
+ int relation_distance;
+};
+
+} // namespace libtextclassifier2
+
+#endif // LIBTEXTCLASSIFIER_TYPES_H_
diff --git a/util/base/casts.h b/util/base/casts.h
index 805ee89..a1d2056 100644
--- a/util/base/casts.h
+++ b/util/base/casts.h
@@ -19,7 +19,7 @@
#include <string.h> // for memcpy
-namespace libtextclassifier {
+namespace libtextclassifier2 {
// bit_cast<Dest, Source> is a template function that implements the equivalent
// of "*reinterpret_cast<Dest*>(&source)". We need this in very low-level
@@ -87,6 +87,6 @@
return dest;
}
-} // namespace libtextclassifier
+} // namespace libtextclassifier2
#endif // LIBTEXTCLASSIFIER_UTIL_BASE_CASTS_H_
diff --git a/util/base/config.h b/util/base/config.h
index e6c19a4..8844b14 100644
--- a/util/base/config.h
+++ b/util/base/config.h
@@ -19,7 +19,7 @@
#ifndef LIBTEXTCLASSIFIER_UTIL_BASE_CONFIG_H_
#define LIBTEXTCLASSIFIER_UTIL_BASE_CONFIG_H_
-namespace libtextclassifier {
+namespace libtextclassifier2 {
// Define LANG_CXX11 to 1 if current compiler supports C++11.
//
@@ -38,6 +38,6 @@
#define LANG_CXX11 1
#endif
-} // namespace libtextclassifier
+} // namespace libtextclassifier2
#endif // LIBTEXTCLASSIFIER_UTIL_BASE_CONFIG_H_
diff --git a/util/base/endian.h b/util/base/endian.h
index 75f8bf7..2dfbfd6 100644
--- a/util/base/endian.h
+++ b/util/base/endian.h
@@ -19,7 +19,7 @@
#include "util/base/integral_types.h"
-namespace libtextclassifier {
+namespace libtextclassifier2 {
#if defined OS_LINUX || defined OS_CYGWIN || defined OS_ANDROID || \
defined(__ANDROID__)
@@ -133,6 +133,6 @@
#endif /* ENDIAN */
};
-} // namespace libtextclassifier
+} // namespace libtextclassifier2
#endif // LIBTEXTCLASSIFIER_UTIL_BASE_ENDIAN_H_
diff --git a/util/base/integral_types.h b/util/base/integral_types.h
index 0322d33..f82c9cd 100644
--- a/util/base/integral_types.h
+++ b/util/base/integral_types.h
@@ -21,7 +21,7 @@
#include "util/base/config.h"
-namespace libtextclassifier {
+namespace libtextclassifier2 {
typedef unsigned int uint32;
typedef unsigned long long uint64;
@@ -56,6 +56,6 @@
static_assert(sizeof(int64) == 8, "wrong size");
#endif // LANG_CXX11
-} // namespace libtextclassifier
+} // namespace libtextclassifier2
#endif // LIBTEXTCLASSIFIER_UTIL_BASE_INTEGRAL_TYPES_H_
diff --git a/util/base/logging.cc b/util/base/logging.cc
index 9de35ca..919bb36 100644
--- a/util/base/logging.cc
+++ b/util/base/logging.cc
@@ -22,7 +22,7 @@
#include "util/base/logging_raw.h"
-namespace libtextclassifier {
+namespace libtextclassifier2 {
namespace logging {
namespace {
@@ -57,12 +57,11 @@
}
LogMessage::~LogMessage() {
- const std::string message = stream_.str();
- LowLevelLogging(severity_, /* tag = */ "txtClsf", message);
+ LowLevelLogging(severity_, /* tag = */ "txtClsf", stream_.message);
if (severity_ == FATAL) {
exit(1);
}
}
} // namespace logging
-} // namespace libtextclassifier
+} // namespace libtextclassifier2
diff --git a/util/base/logging.h b/util/base/logging.h
index dba0ed4..4391d46 100644
--- a/util/base/logging.h
+++ b/util/base/logging.h
@@ -18,32 +18,52 @@
#define LIBTEXTCLASSIFIER_UTIL_BASE_LOGGING_H_
#include <cassert>
-#include <sstream>
#include <string>
#include "util/base/logging_levels.h"
#include "util/base/port.h"
-// TC_STRIP
-namespace libtextclassifier {
-// string class that can't be instantiated. Makes sure that the code does not
-// compile when non std::string is used.
-//
-// NOTE: defined here because most files directly or transitively include this
-// file. Asking people to include a special header just to make sure they don't
-// use the unqualified string doesn't work: as that header doesn't produce any
-// immediate benefit, one can easily forget about it.
-class string {
- public:
- // Makes the class non-instantiable.
- virtual ~string() = 0;
-};
-} // namespace libtextclassifier
-// TC_END_STRIP
-namespace libtextclassifier {
+namespace libtextclassifier2 {
namespace logging {
+// A tiny code footprint string stream for assembling log messages.
+struct LoggingStringStream {
+ LoggingStringStream() {}
+ LoggingStringStream &stream() { return *this; }
+ // Needed for invocation in TC_CHECK macro.
+ explicit operator bool() const { return true; }
+
+ std::string message;
+};
+
+template <typename T>
+inline LoggingStringStream &operator<<(LoggingStringStream &stream,
+ const T &entry) {
+ stream.message.append(std::to_string(entry));
+ return stream;
+}
+
+inline LoggingStringStream &operator<<(LoggingStringStream &stream,
+ const char *message) {
+ stream.message.append(message);
+ return stream;
+}
+
+#if defined(HAS_GLOBAL_STRING)
+inline LoggingStringStream &operator<<(LoggingStringStream &stream,
+ const ::string &message) {
+ stream.message.append(message);
+ return stream;
+}
+#endif
+
+inline LoggingStringStream &operator<<(LoggingStringStream &stream,
+ const std::string &message) {
+ stream.message.append(message);
+ return stream;
+}
+
// The class that does all the work behind our TC_LOG(severity) macros. Each
// TC_LOG(severity) << obj1 << obj2 << ...; logging statement creates a
// LogMessage temporary object containing a stringstream. Each operator<< adds
@@ -61,19 +81,34 @@
~LogMessage() TC_ATTRIBUTE_NOINLINE;
// Returns the stream associated with the logger object.
- std::stringstream &stream() { return stream_; }
+ LoggingStringStream &stream() { return stream_; }
private:
const LogSeverity severity_;
// Stream that "prints" all info into a string (not to a file). We construct
// here the entire logging message and next print it in one operation.
- std::stringstream stream_;
+ LoggingStringStream stream_;
};
-#define TC_LOG(severity) \
- ::libtextclassifier::logging::LogMessage( \
- ::libtextclassifier::logging::severity, __FILE__, __LINE__) \
+// Pseudo-stream that "eats" the tokens <<-pumped into it, without printing
+// anything.
+class NullStream {
+ public:
+ NullStream() {}
+ NullStream &stream() { return *this; }
+};
+template <typename T>
+inline NullStream &operator<<(NullStream &str, const T &) {
+ return str;
+}
+
+} // namespace logging
+} // namespace libtextclassifier2
+
+#define TC_LOG(severity) \
+ ::libtextclassifier2::logging::LogMessage( \
+ ::libtextclassifier2::logging::severity, __FILE__, __LINE__) \
.stream()
// If condition x is true, does nothing. Otherwise, crashes the program (liek
@@ -92,19 +127,7 @@
#define TC_CHECK_GE(x, y) TC_CHECK((x) >= (y))
#define TC_CHECK_NE(x, y) TC_CHECK((x) != (y))
-// Pseudo-stream that "eats" the tokens <<-pumped into it, without printing
-// anything.
-class NullStream {
- public:
- NullStream() {}
- NullStream &stream() { return *this; }
-};
-template <typename T>
-inline NullStream &operator<<(NullStream &str, const T &) {
- return str;
-}
-
-#define TC_NULLSTREAM ::libtextclassifier::logging::NullStream().stream()
+#define TC_NULLSTREAM ::libtextclassifier2::logging::NullStream().stream()
// Debug checks: a TC_DCHECK<suffix> macro should behave like TC_CHECK<suffix>
// in debug mode an don't check / don't print anything in non-debug mode.
@@ -133,15 +156,12 @@
#endif // NDEBUG
#ifdef LIBTEXTCLASSIFIER_VLOG
-#define TC_VLOG(severity) \
- ::libtextclassifier::logging::LogMessage(::libtextclassifier::logging::INFO, \
- __FILE__, __LINE__) \
+#define TC_VLOG(severity) \
+ ::libtextclassifier2::logging::LogMessage( \
+ ::libtextclassifier2::logging::INFO, __FILE__, __LINE__) \
.stream()
#else
#define TC_VLOG(severity) TC_NULLSTREAM
#endif
-} // namespace logging
-} // namespace libtextclassifier
-
#endif // LIBTEXTCLASSIFIER_UTIL_BASE_LOGGING_H_
diff --git a/util/base/logging_levels.h b/util/base/logging_levels.h
index d16f96a..17c882f 100644
--- a/util/base/logging_levels.h
+++ b/util/base/logging_levels.h
@@ -17,7 +17,7 @@
#ifndef LIBTEXTCLASSIFIER_UTIL_BASE_LOGGING_LEVELS_H_
#define LIBTEXTCLASSIFIER_UTIL_BASE_LOGGING_LEVELS_H_
-namespace libtextclassifier {
+namespace libtextclassifier2 {
namespace logging {
enum LogSeverity {
@@ -28,6 +28,6 @@
};
} // namespace logging
-} // namespace libtextclassifier
+} // namespace libtextclassifier2
#endif // LIBTEXTCLASSIFIER_UTIL_BASE_LOGGING_LEVELS_H_
diff --git a/util/base/logging_raw.cc b/util/base/logging_raw.cc
index 8e0eb1b..6d97852 100644
--- a/util/base/logging_raw.cc
+++ b/util/base/logging_raw.cc
@@ -26,7 +26,7 @@
// Compiled as part of Android.
#include <android/log.h>
-namespace libtextclassifier {
+namespace libtextclassifier2 {
namespace logging {
namespace {
@@ -60,12 +60,12 @@
}
} // namespace logging
-} // namespace libtextclassifier
+} // namespace libtextclassifier2
#else // if defined(__ANDROID__)
// Not on Android: implement LowLevelLogging to print to stderr (see below).
-namespace libtextclassifier {
+namespace libtextclassifier2 {
namespace logging {
namespace {
@@ -94,6 +94,6 @@
}
} // namespace logging
-} // namespace libtextclassifier
+} // namespace libtextclassifier2
#endif // if defined(__ANDROID__)
diff --git a/util/base/logging_raw.h b/util/base/logging_raw.h
index 40c2497..e6265c7 100644
--- a/util/base/logging_raw.h
+++ b/util/base/logging_raw.h
@@ -21,7 +21,7 @@
#include "util/base/logging_levels.h"
-namespace libtextclassifier {
+namespace libtextclassifier2 {
namespace logging {
// Low-level logging primitive. Logs a message, with the indicated log
@@ -31,6 +31,6 @@
const std::string &message);
} // namespace logging
-} // namespace libtextclassifier
+} // namespace libtextclassifier2
#endif // LIBTEXTCLASSIFIER_UTIL_BASE_LOGGING_RAW_H_
diff --git a/util/base/macros.h b/util/base/macros.h
index aec3a8a..a021ab9 100644
--- a/util/base/macros.h
+++ b/util/base/macros.h
@@ -19,7 +19,7 @@
#include "util/base/config.h"
-namespace libtextclassifier {
+namespace libtextclassifier2 {
#if LANG_CXX11
#define TC_DISALLOW_COPY_AND_ASSIGN(TypeName) \
@@ -68,16 +68,18 @@
//
// In either case this macro has no effect on runtime behavior and performance
// of code.
-#if defined(__clang__) && defined(LANG_CXX11) && defined(__has_warning)
+#if defined(__clang__) && defined(__has_warning)
#if __has_feature(cxx_attributes) && __has_warning("-Wimplicit-fallthrough")
-#define TC_FALLTHROUGH_INTENDED [[clang::fallthrough]] // NOLINT
+#define TC_FALLTHROUGH_INTENDED [[clang::fallthrough]]
#endif
+#elif defined(__GNUC__) && __GNUC__ >= 7
+#define TC_FALLTHROUGH_INTENDED [[gnu::fallthrough]]
#endif
#ifndef TC_FALLTHROUGH_INTENDED
#define TC_FALLTHROUGH_INTENDED do { } while (0)
#endif
-} // namespace libtextclassifier
+} // namespace libtextclassifier2
#endif // LIBTEXTCLASSIFIER_UTIL_BASE_MACROS_H_
diff --git a/util/base/port.h b/util/base/port.h
index 394aaab..90a2bce 100644
--- a/util/base/port.h
+++ b/util/base/port.h
@@ -19,7 +19,7 @@
#ifndef LIBTEXTCLASSIFIER_UTIL_BASE_PORT_H_
#define LIBTEXTCLASSIFIER_UTIL_BASE_PORT_H_
-namespace libtextclassifier {
+namespace libtextclassifier2 {
#if defined(__GNUC__) && \
(__GNUC__ > 3 || (__GNUC__ == 3 && __GNUC_MINOR__ >= 1))
@@ -40,6 +40,6 @@
#define TC_ATTRIBUTE_NOINLINE
#endif
-} // namespace libtextclassifier
+} // namespace libtextclassifier2
#endif // LIBTEXTCLASSIFIER_UTIL_BASE_PORT_H_
diff --git a/util/calendar/calendar-icu.cc b/util/calendar/calendar-icu.cc
new file mode 100644
index 0000000..34ea22d
--- /dev/null
+++ b/util/calendar/calendar-icu.cc
@@ -0,0 +1,436 @@
+/*
+ * Copyright (C) 2017 The Android Open Source Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "util/calendar/calendar-icu.h"
+
+#include <memory>
+
+#include "util/base/macros.h"
+#include "unicode/gregocal.h"
+#include "unicode/timezone.h"
+#include "unicode/ucal.h"
+
+namespace libtextclassifier2 {
+namespace {
+int MapToDayOfWeekOrDefault(int relation_type, int default_value) {
+ switch (relation_type) {
+ case DateParseData::MONDAY:
+ return UCalendarDaysOfWeek::UCAL_MONDAY;
+ case DateParseData::TUESDAY:
+ return UCalendarDaysOfWeek::UCAL_TUESDAY;
+ case DateParseData::WEDNESDAY:
+ return UCalendarDaysOfWeek::UCAL_WEDNESDAY;
+ case DateParseData::THURSDAY:
+ return UCalendarDaysOfWeek::UCAL_THURSDAY;
+ case DateParseData::FRIDAY:
+ return UCalendarDaysOfWeek::UCAL_FRIDAY;
+ case DateParseData::SATURDAY:
+ return UCalendarDaysOfWeek::UCAL_SATURDAY;
+ case DateParseData::SUNDAY:
+ return UCalendarDaysOfWeek::UCAL_SUNDAY;
+ default:
+ return default_value;
+ }
+}
+
+bool DispatchToRecedeOrToLastDayOfWeek(icu::Calendar* date, int relation_type,
+ int distance) {
+ UErrorCode status = U_ZERO_ERROR;
+ switch (relation_type) {
+ case DateParseData::MONDAY:
+ case DateParseData::TUESDAY:
+ case DateParseData::WEDNESDAY:
+ case DateParseData::THURSDAY:
+ case DateParseData::FRIDAY:
+ case DateParseData::SATURDAY:
+ case DateParseData::SUNDAY:
+ for (int i = 0; i < distance; i++) {
+ do {
+ if (U_FAILURE(status)) {
+ TC_LOG(ERROR) << "error day of week";
+ return false;
+ }
+ date->add(UCalendarDateFields::UCAL_DAY_OF_MONTH, 1, status);
+ if (U_FAILURE(status)) {
+ TC_LOG(ERROR) << "error adding a day";
+ return false;
+ }
+ } while (date->get(UCalendarDateFields::UCAL_DAY_OF_WEEK, status) !=
+ MapToDayOfWeekOrDefault(relation_type, 1));
+ }
+ return true;
+ case DateParseData::DAY:
+ date->add(UCalendarDateFields::UCAL_DAY_OF_MONTH, -1 * distance, status);
+ if (U_FAILURE(status)) {
+ TC_LOG(ERROR) << "error adding a day";
+ return false;
+ }
+
+ return true;
+ case DateParseData::WEEK:
+ date->set(UCalendarDateFields::UCAL_DAY_OF_WEEK, 1);
+ date->add(UCalendarDateFields::UCAL_DAY_OF_MONTH, -7 * (distance - 1),
+ status);
+ if (U_FAILURE(status)) {
+ TC_LOG(ERROR) << "error adding a week";
+ return false;
+ }
+
+ return true;
+ case DateParseData::MONTH:
+ date->set(UCalendarDateFields::UCAL_DAY_OF_MONTH, 1);
+ date->add(UCalendarDateFields::UCAL_MONTH, -1 * (distance - 1), status);
+ if (U_FAILURE(status)) {
+ TC_LOG(ERROR) << "error adding a month";
+ return false;
+ }
+ return true;
+ case DateParseData::YEAR:
+ date->set(UCalendarDateFields::UCAL_DAY_OF_YEAR, 1);
+ date->add(UCalendarDateFields::UCAL_YEAR, -1 * (distance - 1), status);
+ if (U_FAILURE(status)) {
+ TC_LOG(ERROR) << "error adding a year";
+
+ return true;
+ default:
+ return false;
+ }
+ return false;
+ }
+}
+
+bool DispatchToAdvancerOrToNextOrSameDayOfWeek(icu::Calendar* date,
+ int relation_type) {
+ UErrorCode status = U_ZERO_ERROR;
+ switch (relation_type) {
+ case DateParseData::MONDAY:
+ case DateParseData::TUESDAY:
+ case DateParseData::WEDNESDAY:
+ case DateParseData::THURSDAY:
+ case DateParseData::FRIDAY:
+ case DateParseData::SATURDAY:
+ case DateParseData::SUNDAY:
+ while (date->get(UCalendarDateFields::UCAL_DAY_OF_WEEK, status) !=
+ MapToDayOfWeekOrDefault(relation_type, 1)) {
+ if (U_FAILURE(status)) {
+ TC_LOG(ERROR) << "error day of week";
+ return false;
+ }
+ date->add(UCalendarDateFields::UCAL_DAY_OF_MONTH, 1, status);
+ if (U_FAILURE(status)) {
+ TC_LOG(ERROR) << "error adding a day";
+ return false;
+ }
+ }
+ return true;
+ case DateParseData::DAY:
+ date->add(UCalendarDateFields::UCAL_DAY_OF_MONTH, 1, status);
+ if (U_FAILURE(status)) {
+ TC_LOG(ERROR) << "error adding a day";
+ return false;
+ }
+
+ return true;
+ case DateParseData::WEEK:
+ date->set(UCalendarDateFields::UCAL_DAY_OF_WEEK, 1);
+ date->add(UCalendarDateFields::UCAL_DAY_OF_MONTH, 7, status);
+ if (U_FAILURE(status)) {
+ TC_LOG(ERROR) << "error adding a week";
+ return false;
+ }
+
+ return true;
+ case DateParseData::MONTH:
+ date->set(UCalendarDateFields::UCAL_DAY_OF_MONTH, 1);
+ date->add(UCalendarDateFields::UCAL_MONTH, 1, status);
+ if (U_FAILURE(status)) {
+ TC_LOG(ERROR) << "error adding a month";
+ return false;
+ }
+ return true;
+ case DateParseData::YEAR:
+ date->set(UCalendarDateFields::UCAL_DAY_OF_YEAR, 1);
+ date->add(UCalendarDateFields::UCAL_YEAR, 1, status);
+ if (U_FAILURE(status)) {
+ TC_LOG(ERROR) << "error adding a year";
+
+ return true;
+ default:
+ return false;
+ }
+ return false;
+ }
+}
+
+bool DispatchToAdvancerOrToNextDayOfWeek(icu::Calendar* date, int relation_type,
+ int distance) {
+ UErrorCode status = U_ZERO_ERROR;
+ switch (relation_type) {
+ case DateParseData::MONDAY:
+ case DateParseData::TUESDAY:
+ case DateParseData::WEDNESDAY:
+ case DateParseData::THURSDAY:
+ case DateParseData::FRIDAY:
+ case DateParseData::SATURDAY:
+ case DateParseData::SUNDAY:
+ for (int i = 0; i < distance; i++) {
+ do {
+ if (U_FAILURE(status)) {
+ TC_LOG(ERROR) << "error day of week";
+ return false;
+ }
+ date->add(UCalendarDateFields::UCAL_DAY_OF_MONTH, 1, status);
+ if (U_FAILURE(status)) {
+ TC_LOG(ERROR) << "error adding a day";
+ return false;
+ }
+ } while (date->get(UCalendarDateFields::UCAL_DAY_OF_WEEK, status) !=
+ MapToDayOfWeekOrDefault(relation_type, 1));
+ }
+ return true;
+ case DateParseData::DAY:
+ date->add(UCalendarDateFields::UCAL_DAY_OF_MONTH, distance, status);
+ if (U_FAILURE(status)) {
+ TC_LOG(ERROR) << "error adding a day";
+ return false;
+ }
+
+ return true;
+ case DateParseData::WEEK:
+ date->set(UCalendarDateFields::UCAL_DAY_OF_WEEK, 1);
+ date->add(UCalendarDateFields::UCAL_DAY_OF_MONTH, 7 * distance, status);
+ if (U_FAILURE(status)) {
+ TC_LOG(ERROR) << "error adding a week";
+ return false;
+ }
+
+ return true;
+ case DateParseData::MONTH:
+ date->set(UCalendarDateFields::UCAL_DAY_OF_MONTH, 1);
+ date->add(UCalendarDateFields::UCAL_MONTH, 1 * distance, status);
+ if (U_FAILURE(status)) {
+ TC_LOG(ERROR) << "error adding a month";
+ return false;
+ }
+ return true;
+ case DateParseData::YEAR:
+ date->set(UCalendarDateFields::UCAL_DAY_OF_YEAR, 1);
+ date->add(UCalendarDateFields::UCAL_YEAR, 1 * distance, status);
+ if (U_FAILURE(status)) {
+ TC_LOG(ERROR) << "error adding a year";
+
+ return true;
+ default:
+ return false;
+ }
+ return false;
+ }
+}
+
+bool RoundToGranularity(DatetimeGranularity granularity,
+ icu::Calendar* calendar) {
+ // Force recomputation before doing the rounding.
+ UErrorCode status = U_ZERO_ERROR;
+ calendar->get(UCalendarDateFields::UCAL_DAY_OF_WEEK, status);
+ if (U_FAILURE(status)) {
+ TC_LOG(ERROR) << "Can't interpret date.";
+ return false;
+ }
+
+ switch (granularity) {
+ case GRANULARITY_YEAR:
+ calendar->set(UCalendarDateFields::UCAL_MONTH, 0);
+ TC_FALLTHROUGH_INTENDED;
+ case GRANULARITY_MONTH:
+ calendar->set(UCalendarDateFields::UCAL_DAY_OF_MONTH, 1);
+ TC_FALLTHROUGH_INTENDED;
+ case GRANULARITY_DAY:
+ calendar->set(UCalendarDateFields::UCAL_HOUR, 0);
+ TC_FALLTHROUGH_INTENDED;
+ case GRANULARITY_HOUR:
+ calendar->set(UCalendarDateFields::UCAL_MINUTE, 0);
+ TC_FALLTHROUGH_INTENDED;
+ case GRANULARITY_MINUTE:
+ calendar->set(UCalendarDateFields::UCAL_SECOND, 0);
+ break;
+
+ case GRANULARITY_WEEK:
+ calendar->set(UCalendarDateFields::UCAL_DAY_OF_WEEK,
+ calendar->getFirstDayOfWeek());
+ calendar->set(UCalendarDateFields::UCAL_HOUR, 0);
+ calendar->set(UCalendarDateFields::UCAL_MINUTE, 0);
+ calendar->set(UCalendarDateFields::UCAL_SECOND, 0);
+ break;
+
+ case GRANULARITY_UNKNOWN:
+ case GRANULARITY_SECOND:
+ break;
+ }
+
+ return true;
+}
+
+} // namespace
+
+bool CalendarLib::InterpretParseData(const DateParseData& parse_data,
+ int64 reference_time_ms_utc,
+ const std::string& reference_timezone,
+ const std::string& reference_locale,
+ DatetimeGranularity granularity,
+ int64* interpreted_time_ms_utc) const {
+ UErrorCode status = U_ZERO_ERROR;
+
+ std::unique_ptr<icu::Calendar> date(icu::Calendar::createInstance(
+ icu::Locale::createFromName(reference_locale.c_str()), status));
+ if (U_FAILURE(status)) {
+ TC_LOG(ERROR) << "error getting calendar instance";
+ return false;
+ }
+
+ date->adoptTimeZone(icu::TimeZone::createTimeZone(
+ icu::UnicodeString::fromUTF8(reference_timezone)));
+ date->setTime(reference_time_ms_utc, status);
+
+ // By default, the parsed time is interpreted to be on the reference day. But
+ // a parsed date, should have time 0:00:00 unless specified.
+ date->set(UCalendarDateFields::UCAL_HOUR_OF_DAY, 0);
+ date->set(UCalendarDateFields::UCAL_MINUTE, 0);
+ date->set(UCalendarDateFields::UCAL_SECOND, 0);
+ date->set(UCalendarDateFields::UCAL_MILLISECOND, 0);
+
+ static const int64 kMillisInHour = 1000 * 60 * 60;
+ if (parse_data.field_set_mask & DateParseData::Fields::ZONE_OFFSET_FIELD) {
+ date->set(UCalendarDateFields::UCAL_ZONE_OFFSET,
+ parse_data.zone_offset * kMillisInHour);
+ }
+ if (parse_data.field_set_mask & DateParseData::Fields::DST_OFFSET_FIELD) {
+ // convert from hours to milliseconds
+ date->set(UCalendarDateFields::UCAL_DST_OFFSET,
+ parse_data.dst_offset * kMillisInHour);
+ }
+
+ if (parse_data.field_set_mask & DateParseData::Fields::RELATION_FIELD) {
+ switch (parse_data.relation) {
+ case DateParseData::Relation::NEXT:
+ if (parse_data.field_set_mask &
+ DateParseData::Fields::RELATION_TYPE_FIELD) {
+ if (!DispatchToAdvancerOrToNextDayOfWeek(
+ date.get(), parse_data.relation_type, 1)) {
+ return false;
+ }
+ }
+ break;
+ case DateParseData::Relation::NEXT_OR_SAME:
+ if (parse_data.field_set_mask &
+ DateParseData::Fields::RELATION_TYPE_FIELD) {
+ if (!DispatchToAdvancerOrToNextOrSameDayOfWeek(
+ date.get(), parse_data.relation_type)) {
+ return false;
+ }
+ }
+ break;
+ case DateParseData::Relation::LAST:
+ if (parse_data.field_set_mask &
+ DateParseData::Fields::RELATION_TYPE_FIELD) {
+ if (!DispatchToRecedeOrToLastDayOfWeek(date.get(),
+ parse_data.relation_type, 1)) {
+ return false;
+ }
+ }
+ break;
+ case DateParseData::Relation::NOW:
+ // NOOP
+ break;
+ case DateParseData::Relation::TOMORROW:
+ date->add(UCalendarDateFields::UCAL_DAY_OF_MONTH, 1, status);
+ if (U_FAILURE(status)) {
+ TC_LOG(ERROR) << "error adding a day";
+ return false;
+ }
+ break;
+ case DateParseData::Relation::YESTERDAY:
+ date->add(UCalendarDateFields::UCAL_DAY_OF_MONTH, -1, status);
+ if (U_FAILURE(status)) {
+ TC_LOG(ERROR) << "error subtracting a day";
+ return false;
+ }
+ break;
+ case DateParseData::Relation::PAST:
+ if (parse_data.field_set_mask &
+ DateParseData::Fields::RELATION_TYPE_FIELD) {
+ if (parse_data.field_set_mask &
+ DateParseData::Fields::RELATION_DISTANCE_FIELD) {
+ if (!DispatchToRecedeOrToLastDayOfWeek(
+ date.get(), parse_data.relation_type,
+ parse_data.relation_distance)) {
+ return false;
+ }
+ }
+ }
+ break;
+ case DateParseData::Relation::FUTURE:
+ if (parse_data.field_set_mask &
+ DateParseData::Fields::RELATION_TYPE_FIELD) {
+ if (parse_data.field_set_mask &
+ DateParseData::Fields::RELATION_DISTANCE_FIELD) {
+ if (!DispatchToAdvancerOrToNextDayOfWeek(
+ date.get(), parse_data.relation_type,
+ parse_data.relation_distance)) {
+ return false;
+ }
+ }
+ }
+ break;
+ }
+ }
+ if (parse_data.field_set_mask & DateParseData::Fields::YEAR_FIELD) {
+ date->set(UCalendarDateFields::UCAL_YEAR, parse_data.year);
+ }
+ if (parse_data.field_set_mask & DateParseData::Fields::MONTH_FIELD) {
+ // NOTE: Java and ICU disagree on month formats
+ date->set(UCalendarDateFields::UCAL_MONTH, parse_data.month - 1);
+ }
+ if (parse_data.field_set_mask & DateParseData::Fields::DAY_FIELD) {
+ date->set(UCalendarDateFields::UCAL_DAY_OF_MONTH, parse_data.day_of_month);
+ }
+ if (parse_data.field_set_mask & DateParseData::Fields::HOUR_FIELD) {
+ if (parse_data.field_set_mask & DateParseData::Fields::AMPM_FIELD &&
+ parse_data.ampm == 1 && parse_data.hour < 12) {
+ date->set(UCalendarDateFields::UCAL_HOUR_OF_DAY, parse_data.hour + 12);
+ } else {
+ date->set(UCalendarDateFields::UCAL_HOUR_OF_DAY, parse_data.hour);
+ }
+ }
+ if (parse_data.field_set_mask & DateParseData::Fields::MINUTE_FIELD) {
+ date->set(UCalendarDateFields::UCAL_MINUTE, parse_data.minute);
+ }
+ if (parse_data.field_set_mask & DateParseData::Fields::SECOND_FIELD) {
+ date->set(UCalendarDateFields::UCAL_SECOND, parse_data.second);
+ }
+
+ if (!RoundToGranularity(granularity, date.get())) {
+ return false;
+ }
+
+ *interpreted_time_ms_utc = date->getTime(status);
+ if (U_FAILURE(status)) {
+ TC_LOG(ERROR) << "error getting time from instance";
+ return false;
+ }
+
+ return true;
+}
+} // namespace libtextclassifier2
diff --git a/util/calendar/calendar-icu.h b/util/calendar/calendar-icu.h
new file mode 100644
index 0000000..8aae7ab
--- /dev/null
+++ b/util/calendar/calendar-icu.h
@@ -0,0 +1,41 @@
+/*
+ * Copyright (C) 2017 The Android Open Source Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#ifndef LIBTEXTCLASSIFIER_UTIL_CALENDAR_CALENDAR_ICU_H_
+#define LIBTEXTCLASSIFIER_UTIL_CALENDAR_CALENDAR_ICU_H_
+
+#include <string>
+
+#include "types.h"
+#include "util/base/integral_types.h"
+#include "util/base/logging.h"
+
+namespace libtextclassifier2 {
+
+class CalendarLib {
+ public:
+ // Interprets parse_data as milliseconds since_epoch. Relative times are
+ // resolved against the current time (reference_time_ms_utc). Returns true if
+ // the interpratation was successful, false otherwise.
+ bool InterpretParseData(const DateParseData& parse_data,
+ int64 reference_time_ms_utc,
+ const std::string& reference_timezone,
+ const std::string& reference_locale,
+ DatetimeGranularity granularity,
+ int64* interpreted_time_ms_utc) const;
+};
+} // namespace libtextclassifier2
+#endif // LIBTEXTCLASSIFIER_UTIL_CALENDAR_CALENDAR_ICU_H_
diff --git a/common/config.h b/util/calendar/calendar.h
similarity index 67%
copy from common/config.h
copy to util/calendar/calendar.h
index b883e95..b0cf2e6 100644
--- a/common/config.h
+++ b/util/calendar/calendar.h
@@ -14,16 +14,9 @@
* limitations under the License.
*/
-#ifndef LIBTEXTCLASSIFIER_COMMON_CONFIG_H_
-#define LIBTEXTCLASSIFIER_COMMON_CONFIG_H_
+#ifndef LIBTEXTCLASSIFIER_UTIL_CALENDAR_CALENDAR_H_
+#define LIBTEXTCLASSIFIER_UTIL_CALENDAR_CALENDAR_H_
-#ifndef PORTABLE_SAFT_MOBILE
-#if defined(__ANDROID__) || defined(__APPLE__)
-#define PORTABLE_SAFT_MOBILE 1
-#else
-#define PORTABLE_SAFT_MOBILE 0
-#endif
+#include "util/calendar/calendar-icu.h"
-#endif
-
-#endif // LIBTEXTCLASSIFIER_COMMON_CONFIG_H_
+#endif // LIBTEXTCLASSIFIER_UTIL_CALENDAR_CALENDAR_H_
diff --git a/util/calendar/calendar_test.cc b/util/calendar/calendar_test.cc
new file mode 100644
index 0000000..1f29106
--- /dev/null
+++ b/util/calendar/calendar_test.cc
@@ -0,0 +1,129 @@
+/*
+ * Copyright (C) 2017 The Android Open Source Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+// This test serves the purpose of making sure all the different implementations
+// of the unspoken CalendarLib interface support the same methods.
+
+#include "util/calendar/calendar.h"
+#include "util/base/logging.h"
+
+#include "gtest/gtest.h"
+
+namespace libtextclassifier2 {
+namespace {
+
+TEST(CalendarTest, Interface) {
+ CalendarLib calendar;
+ int64 time;
+ std::string timezone;
+ bool result = calendar.InterpretParseData(
+ DateParseData{0l, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+ static_cast<DateParseData::Relation>(0),
+ static_cast<DateParseData::RelationType>(0), 0},
+ 0L, "Zurich", "en-CH", GRANULARITY_UNKNOWN, &time);
+ TC_LOG(INFO) << result;
+}
+
+#ifdef LIBTEXTCLASSIFIER_UNILIB_ICU
+TEST(CalendarTest, RoundingToGranularity) {
+ CalendarLib calendar;
+ int64 time;
+ std::string timezone;
+ DateParseData data;
+ data.year = 2018;
+ data.month = 4;
+ data.day_of_month = 25;
+ data.hour = 9;
+ data.minute = 33;
+ data.second = 59;
+ data.field_set_mask = DateParseData::YEAR_FIELD | DateParseData::MONTH_FIELD |
+ DateParseData::DAY_FIELD | DateParseData::HOUR_FIELD |
+ DateParseData::MINUTE_FIELD |
+ DateParseData::SECOND_FIELD;
+ ASSERT_TRUE(calendar.InterpretParseData(
+ data,
+ /*reference_time_ms_utc=*/0L, /*reference_timezone=*/"Europe/Zurich",
+ /*reference_locale=*/"en-CH",
+ /*granularity=*/GRANULARITY_YEAR, &time));
+ EXPECT_EQ(time, 1514761200000L /* Jan 01 2018 00:00:00 */);
+
+ ASSERT_TRUE(calendar.InterpretParseData(
+ data,
+ /*reference_time_ms_utc=*/0L, /*reference_timezone=*/"Europe/Zurich",
+ /*reference_locale=*/"en-CH",
+ /*granularity=*/GRANULARITY_MONTH, &time));
+ EXPECT_EQ(time, 1522533600000L /* Apr 01 2018 00:00:00 */);
+
+ ASSERT_TRUE(calendar.InterpretParseData(
+ data,
+ /*reference_time_ms_utc=*/0L, /*reference_timezone=*/"Europe/Zurich",
+ /*reference_locale=*/"en-CH",
+ /*granularity=*/GRANULARITY_WEEK, &time));
+ EXPECT_EQ(time, 1524434400000L /* Mon Apr 23 2018 00:00:00 */);
+
+ ASSERT_TRUE(calendar.InterpretParseData(
+ data,
+ /*reference_time_ms_utc=*/0L, /*reference_timezone=*/"Europe/Zurich",
+ /*reference_locale=*/"*-CH",
+ /*granularity=*/GRANULARITY_WEEK, &time));
+ EXPECT_EQ(time, 1524434400000L /* Mon Apr 23 2018 00:00:00 */);
+
+ ASSERT_TRUE(calendar.InterpretParseData(
+ data,
+ /*reference_time_ms_utc=*/0L, /*reference_timezone=*/"Europe/Zurich",
+ /*reference_locale=*/"en-US",
+ /*granularity=*/GRANULARITY_WEEK, &time));
+ EXPECT_EQ(time, 1524348000000L /* Sun Apr 22 2018 00:00:00 */);
+
+ ASSERT_TRUE(calendar.InterpretParseData(
+ data,
+ /*reference_time_ms_utc=*/0L, /*reference_timezone=*/"Europe/Zurich",
+ /*reference_locale=*/"*-US",
+ /*granularity=*/GRANULARITY_WEEK, &time));
+ EXPECT_EQ(time, 1524348000000L /* Sun Apr 22 2018 00:00:00 */);
+
+ ASSERT_TRUE(calendar.InterpretParseData(
+ data,
+ /*reference_time_ms_utc=*/0L, /*reference_timezone=*/"Europe/Zurich",
+ /*reference_locale=*/"en-CH",
+ /*granularity=*/GRANULARITY_DAY, &time));
+ EXPECT_EQ(time, 1524607200000L /* Apr 25 2018 00:00:00 */);
+
+ ASSERT_TRUE(calendar.InterpretParseData(
+ data,
+ /*reference_time_ms_utc=*/0L, /*reference_timezone=*/"Europe/Zurich",
+ /*reference_locale=*/"en-CH",
+ /*granularity=*/GRANULARITY_HOUR, &time));
+ EXPECT_EQ(time, 1524639600000L /* Apr 25 2018 09:00:00 */);
+
+ ASSERT_TRUE(calendar.InterpretParseData(
+ data,
+ /*reference_time_ms_utc=*/0L, /*reference_timezone=*/"Europe/Zurich",
+ /*reference_locale=*/"en-CH",
+ /*granularity=*/GRANULARITY_MINUTE, &time));
+ EXPECT_EQ(time, 1524641580000 /* Apr 25 2018 09:33:00 */);
+
+ ASSERT_TRUE(calendar.InterpretParseData(
+ data,
+ /*reference_time_ms_utc=*/0L, /*reference_timezone=*/"Europe/Zurich",
+ /*reference_locale=*/"en-CH",
+ /*granularity=*/GRANULARITY_SECOND, &time));
+ EXPECT_EQ(time, 1524641639000 /* Apr 25 2018 09:33:59 */);
+}
+#endif // LIBTEXTCLASSIFIER_UNILIB_DUMMY
+
+} // namespace
+} // namespace libtextclassifier2
diff --git a/common/config.h b/util/flatbuffers.cc
similarity index 67%
copy from common/config.h
copy to util/flatbuffers.cc
index b883e95..6c0108e 100644
--- a/common/config.h
+++ b/util/flatbuffers.cc
@@ -14,16 +14,13 @@
* limitations under the License.
*/
-#ifndef LIBTEXTCLASSIFIER_COMMON_CONFIG_H_
-#define LIBTEXTCLASSIFIER_COMMON_CONFIG_H_
+#include "util/flatbuffers.h"
-#ifndef PORTABLE_SAFT_MOBILE
-#if defined(__ANDROID__) || defined(__APPLE__)
-#define PORTABLE_SAFT_MOBILE 1
-#else
-#define PORTABLE_SAFT_MOBILE 0
-#endif
+namespace libtextclassifier2 {
-#endif
+template <>
+const char* FlatbufferFileIdentifier<Model>() {
+ return ModelIdentifier();
+}
-#endif // LIBTEXTCLASSIFIER_COMMON_CONFIG_H_
+} // namespace libtextclassifier2
diff --git a/util/flatbuffers.h b/util/flatbuffers.h
new file mode 100644
index 0000000..93d73b6
--- /dev/null
+++ b/util/flatbuffers.h
@@ -0,0 +1,98 @@
+/*
+ * Copyright (C) 2017 The Android Open Source Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+// Utility functions for working with FlatBuffers.
+
+#ifndef LIBTEXTCLASSIFIER_UTIL_FLATBUFFERS_H_
+#define LIBTEXTCLASSIFIER_UTIL_FLATBUFFERS_H_
+
+#include <memory>
+#include <string>
+
+#include "model_generated.h"
+#include "flatbuffers/flatbuffers.h"
+
+namespace libtextclassifier2 {
+
+// Loads and interprets the buffer as 'FlatbufferMessage' and verifies its
+// integrity.
+template <typename FlatbufferMessage>
+const FlatbufferMessage* LoadAndVerifyFlatbuffer(const void* buffer, int size) {
+ const FlatbufferMessage* message =
+ flatbuffers::GetRoot<FlatbufferMessage>(buffer);
+ if (message == nullptr) {
+ return nullptr;
+ }
+ flatbuffers::Verifier verifier(reinterpret_cast<const uint8_t*>(buffer),
+ size);
+ if (message->Verify(verifier)) {
+ return message;
+ } else {
+ return nullptr;
+ }
+}
+
+// Same as above but takes string.
+template <typename FlatbufferMessage>
+const FlatbufferMessage* LoadAndVerifyFlatbuffer(const std::string& buffer) {
+ return LoadAndVerifyFlatbuffer<FlatbufferMessage>(buffer.c_str(),
+ buffer.size());
+}
+
+// Loads and interprets the buffer as 'FlatbufferMessage', verifies its
+// integrity and returns its mutable version.
+template <typename FlatbufferMessage>
+std::unique_ptr<typename FlatbufferMessage::NativeTableType>
+LoadAndVerifyMutableFlatbuffer(const void* buffer, int size) {
+ const FlatbufferMessage* message =
+ LoadAndVerifyFlatbuffer<FlatbufferMessage>(buffer, size);
+ if (message == nullptr) {
+ return nullptr;
+ }
+ return std::unique_ptr<typename FlatbufferMessage::NativeTableType>(
+ message->UnPack());
+}
+
+// Same as above but takes string.
+template <typename FlatbufferMessage>
+std::unique_ptr<typename FlatbufferMessage::NativeTableType>
+LoadAndVerifyMutableFlatbuffer(const std::string& buffer) {
+ return LoadAndVerifyMutableFlatbuffer<FlatbufferMessage>(buffer.c_str(),
+ buffer.size());
+}
+
+template <typename FlatbufferMessage>
+const char* FlatbufferFileIdentifier() {
+ return nullptr;
+}
+
+template <>
+const char* FlatbufferFileIdentifier<Model>();
+
+// Packs the mutable flatbuffer message to string.
+template <typename FlatbufferMessage>
+std::string PackFlatbuffer(
+ const typename FlatbufferMessage::NativeTableType* mutable_message) {
+ flatbuffers::FlatBufferBuilder builder;
+ builder.Finish(FlatbufferMessage::Pack(builder, mutable_message),
+ FlatbufferFileIdentifier<FlatbufferMessage>());
+ return std::string(reinterpret_cast<const char*>(builder.GetBufferPointer()),
+ builder.GetSize());
+}
+
+} // namespace libtextclassifier2
+
+#endif // LIBTEXTCLASSIFIER_UTIL_FLATBUFFERS_H_
diff --git a/util/gtl/map_util.h b/util/gtl/map_util.h
index b5eaafa..bd020f8 100644
--- a/util/gtl/map_util.h
+++ b/util/gtl/map_util.h
@@ -17,7 +17,7 @@
#ifndef LIBTEXTCLASSIFIER_UTIL_GTL_MAP_UTIL_H_
#define LIBTEXTCLASSIFIER_UTIL_GTL_MAP_UTIL_H_
-namespace libtextclassifier {
+namespace libtextclassifier2 {
// Returns a const reference to the value associated with the given key if it
// exists, otherwise returns a const reference to the provided default value.
@@ -60,6 +60,6 @@
typename Collection::value_type(key, value));
}
-} // namespace libtextclassifier
+} // namespace libtextclassifier2
#endif // LIBTEXTCLASSIFIER_UTIL_GTL_MAP_UTIL_H_
diff --git a/util/gtl/stl_util.h b/util/gtl/stl_util.h
index 8e1c452..7b88e05 100644
--- a/util/gtl/stl_util.h
+++ b/util/gtl/stl_util.h
@@ -17,7 +17,7 @@
#ifndef LIBTEXTCLASSIFIER_UTIL_GTL_STL_UTIL_H_
#define LIBTEXTCLASSIFIER_UTIL_GTL_STL_UTIL_H_
-namespace libtextclassifier {
+namespace libtextclassifier2 {
// Deletes all the elements in an STL container and clears the container. This
// function is suitable for use with a vector, set, hash_set, or any other STL
@@ -50,6 +50,6 @@
container->clear();
}
-} // namespace libtextclassifier
+} // namespace libtextclassifier2
#endif // LIBTEXTCLASSIFIER_UTIL_GTL_STL_UTIL_H_
diff --git a/util/hash/farmhash.h b/util/hash/farmhash.h
index 7adf3aa..477b7a8 100644
--- a/util/hash/farmhash.h
+++ b/util/hash/farmhash.h
@@ -24,7 +24,7 @@
#include <utility>
#ifndef NAMESPACE_FOR_HASH_FUNCTIONS
-#define NAMESPACE_FOR_HASH_FUNCTIONS tcfarmhash
+#define NAMESPACE_FOR_HASH_FUNCTIONS tc2farmhash
#endif
namespace NAMESPACE_FOR_HASH_FUNCTIONS {
diff --git a/util/hash/hash.cc b/util/hash/hash.cc
index 1261417..9722ddc 100644
--- a/util/hash/hash.cc
+++ b/util/hash/hash.cc
@@ -18,7 +18,7 @@
#include "util/base/macros.h"
-namespace libtextclassifier {
+namespace libtextclassifier2 {
namespace {
// Lower-level versions of Get... that read directly from a character buffer
@@ -76,4 +76,4 @@
return h;
}
-} // namespace libtextclassifier
+} // namespace libtextclassifier2
diff --git a/util/hash/hash.h b/util/hash/hash.h
index 0abb72b..b7a3b53 100644
--- a/util/hash/hash.h
+++ b/util/hash/hash.h
@@ -21,7 +21,7 @@
#include "util/base/integral_types.h"
-namespace libtextclassifier {
+namespace libtextclassifier2 {
uint32 Hash32(const char *data, size_t n, uint32 seed);
@@ -33,6 +33,6 @@
return Hash32WithDefaultSeed(input.data(), input.size());
}
-} // namespace libtextclassifier
+} // namespace libtextclassifier2
#endif // LIBTEXTCLASSIFIER_UTIL_HASH_HASH_H_
diff --git a/util/i18n/locale.cc b/util/i18n/locale.cc
new file mode 100644
index 0000000..c587d2d
--- /dev/null
+++ b/util/i18n/locale.cc
@@ -0,0 +1,110 @@
+/*
+ * Copyright (C) 2017 The Android Open Source Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "util/i18n/locale.h"
+
+#include "util/strings/split.h"
+
+namespace libtextclassifier2 {
+
+namespace {
+
+bool CheckLanguage(StringPiece language) {
+ if (language.size() != 2 && language.size() != 3) {
+ return false;
+ }
+
+ // Needs to be all lowercase.
+ for (int i = 0; i < language.size(); ++i) {
+ if (!std::islower(language[i])) {
+ return false;
+ }
+ }
+
+ return true;
+}
+
+bool CheckScript(StringPiece script) {
+ if (script.size() != 4) {
+ return false;
+ }
+
+ if (!std::isupper(script[0])) {
+ return false;
+ }
+
+ // Needs to be all lowercase.
+ for (int i = 1; i < script.size(); ++i) {
+ if (!std::islower(script[i])) {
+ return false;
+ }
+ }
+
+ return true;
+}
+
+bool CheckRegion(StringPiece region) {
+ if (region.size() == 2) {
+ return std::isupper(region[0]) && std::isupper(region[1]);
+ } else if (region.size() == 3) {
+ return std::isdigit(region[0]) && std::isdigit(region[1]) &&
+ std::isdigit(region[2]);
+ } else {
+ return false;
+ }
+}
+
+} // namespace
+
+Locale Locale::FromBCP47(const std::string& locale_tag) {
+ std::vector<StringPiece> parts = strings::Split(locale_tag, '-');
+ if (parts.empty()) {
+ return Locale::Invalid();
+ }
+
+ auto parts_it = parts.begin();
+ StringPiece language = *parts_it;
+ if (!CheckLanguage(language)) {
+ return Locale::Invalid();
+ }
+ ++parts_it;
+
+ StringPiece script;
+ if (parts_it != parts.end()) {
+ script = *parts_it;
+ if (!CheckScript(script)) {
+ script = "";
+ } else {
+ ++parts_it;
+ }
+ }
+
+ StringPiece region;
+ if (parts_it != parts.end()) {
+ region = *parts_it;
+ if (!CheckRegion(region)) {
+ region = "";
+ } else {
+ ++parts_it;
+ }
+ }
+
+ // NOTE: We don't parse the rest of the BCP47 tag here even if specified.
+
+ return Locale(language.ToString(), script.ToString(), region.ToString());
+}
+
+} // namespace libtextclassifier2
diff --git a/util/i18n/locale.h b/util/i18n/locale.h
new file mode 100644
index 0000000..16f10dc
--- /dev/null
+++ b/util/i18n/locale.h
@@ -0,0 +1,63 @@
+/*
+ * Copyright (C) 2017 The Android Open Source Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#ifndef LIBTEXTCLASSIFIER_UTIL_I18N_LOCALE_H_
+#define LIBTEXTCLASSIFIER_UTIL_I18N_LOCALE_H_
+
+#include <string>
+
+#include "util/base/integral_types.h"
+
+namespace libtextclassifier2 {
+
+class Locale {
+ public:
+ // Constructs the object from a valid BCP47 tag. If the tag is invalid,
+ // an object is created that gives false when IsInvalid() is called.
+ static Locale FromBCP47(const std::string& locale_tag);
+
+ // Creates a prototypical invalid locale object.
+ static Locale Invalid() {
+ Locale locale(/*language=*/"", /*script=*/"", /*region=*/"");
+ locale.is_valid_ = false;
+ return locale;
+ }
+
+ std::string Language() const { return language_; }
+
+ std::string Script() const { return script_; }
+
+ std::string Region() const { return region_; }
+
+ bool IsValid() const { return is_valid_; }
+
+ private:
+ Locale(const std::string& language, const std::string& script,
+ const std::string& region)
+ : language_(language),
+ script_(script),
+ region_(region),
+ is_valid_(true) {}
+
+ std::string language_;
+ std::string script_;
+ std::string region_;
+ bool is_valid_;
+};
+
+} // namespace libtextclassifier2
+
+#endif // LIBTEXTCLASSIFIER_UTIL_I18N_LOCALE_H_
diff --git a/util/i18n/locale_test.cc b/util/i18n/locale_test.cc
new file mode 100644
index 0000000..72ece98
--- /dev/null
+++ b/util/i18n/locale_test.cc
@@ -0,0 +1,70 @@
+/*
+ * Copyright (C) 2017 The Android Open Source Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "util/i18n/locale.h"
+
+#include "gtest/gtest.h"
+
+namespace libtextclassifier2 {
+namespace {
+
+TEST(LocaleTest, ParseUnknown) {
+ Locale locale = Locale::Invalid();
+ EXPECT_FALSE(locale.IsValid());
+}
+
+TEST(LocaleTest, ParseSwissEnglish) {
+ Locale locale = Locale::FromBCP47("en-CH");
+ EXPECT_TRUE(locale.IsValid());
+ EXPECT_EQ(locale.Language(), "en");
+ EXPECT_EQ(locale.Script(), "");
+ EXPECT_EQ(locale.Region(), "CH");
+}
+
+TEST(LocaleTest, ParseChineseChina) {
+ Locale locale = Locale::FromBCP47("zh-CN");
+ EXPECT_TRUE(locale.IsValid());
+ EXPECT_EQ(locale.Language(), "zh");
+ EXPECT_EQ(locale.Script(), "");
+ EXPECT_EQ(locale.Region(), "CN");
+}
+
+TEST(LocaleTest, ParseChineseTaiwan) {
+ Locale locale = Locale::FromBCP47("zh-Hant-TW");
+ EXPECT_TRUE(locale.IsValid());
+ EXPECT_EQ(locale.Language(), "zh");
+ EXPECT_EQ(locale.Script(), "Hant");
+ EXPECT_EQ(locale.Region(), "TW");
+}
+
+TEST(LocaleTest, ParseEnglish) {
+ Locale locale = Locale::FromBCP47("en");
+ EXPECT_TRUE(locale.IsValid());
+ EXPECT_EQ(locale.Language(), "en");
+ EXPECT_EQ(locale.Script(), "");
+ EXPECT_EQ(locale.Region(), "");
+}
+
+TEST(LocaleTest, ParseCineseTraditional) {
+ Locale locale = Locale::FromBCP47("zh-Hant");
+ EXPECT_TRUE(locale.IsValid());
+ EXPECT_EQ(locale.Language(), "zh");
+ EXPECT_EQ(locale.Script(), "Hant");
+ EXPECT_EQ(locale.Region(), "");
+}
+
+} // namespace
+} // namespace libtextclassifier2
diff --git a/util/java/scoped_global_ref.h b/util/java/scoped_global_ref.h
new file mode 100644
index 0000000..3f8754d
--- /dev/null
+++ b/util/java/scoped_global_ref.h
@@ -0,0 +1,76 @@
+/*
+ * Copyright (C) 2017 The Android Open Source Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#ifndef LIBTEXTCLASSIFIER_UTIL_JAVA_SCOPED_GLOBAL_REF_H_
+#define LIBTEXTCLASSIFIER_UTIL_JAVA_SCOPED_GLOBAL_REF_H_
+
+#include <jni.h>
+#include <memory>
+#include <type_traits>
+
+#include "util/base/logging.h"
+
+namespace libtextclassifier2 {
+
+// A deleter to be used with std::unique_ptr to delete JNI global references.
+class GlobalRefDeleter {
+ public:
+ GlobalRefDeleter() : jvm_(nullptr) {}
+
+ // Style guide violating implicit constructor so that the GlobalRefDeleter
+ // is implicitly constructed from the second argument to ScopedGlobalRef.
+ GlobalRefDeleter(JavaVM* jvm) : jvm_(jvm) {} // NOLINT(runtime/explicit)
+
+ GlobalRefDeleter(const GlobalRefDeleter& orig) = default;
+
+ // Copy assignment to allow move semantics in ScopedGlobalRef.
+ GlobalRefDeleter& operator=(const GlobalRefDeleter& rhs) {
+ TC_CHECK_EQ(jvm_, rhs.jvm_);
+ return *this;
+ }
+
+ // The delete operator.
+ void operator()(jobject object) const {
+ JNIEnv* env;
+ if (object != nullptr && jvm_ != nullptr &&
+ JNI_OK ==
+ jvm_->GetEnv(reinterpret_cast<void**>(&env), JNI_VERSION_1_4)) {
+ env->DeleteGlobalRef(object);
+ }
+ }
+
+ private:
+ // The jvm_ stashed to use for deletion.
+ JavaVM* const jvm_;
+};
+
+// A smart pointer that deletes a JNI global reference when it goes out
+// of scope. Usage is:
+// ScopedGlobalRef<jobject> scoped_global(env->JniFunction(), jvm);
+template <typename T>
+using ScopedGlobalRef =
+ std::unique_ptr<typename std::remove_pointer<T>::type, GlobalRefDeleter>;
+
+// A helper to create global references.
+template <typename T>
+ScopedGlobalRef<T> MakeGlobalRef(T object, JNIEnv* env, JavaVM* jvm) {
+ const jobject globalObject = env->NewGlobalRef(object);
+ return ScopedGlobalRef<T>(reinterpret_cast<T>(globalObject), jvm);
+}
+
+} // namespace libtextclassifier2
+
+#endif // LIBTEXTCLASSIFIER_UTIL_JAVA_SCOPED_GLOBAL_REF_H_
diff --git a/util/java/scoped_local_ref.h b/util/java/scoped_local_ref.h
index d995468..8476767 100644
--- a/util/java/scoped_local_ref.h
+++ b/util/java/scoped_local_ref.h
@@ -23,11 +23,13 @@
#include "util/base/logging.h"
-namespace libtextclassifier {
+namespace libtextclassifier2 {
// A deleter to be used with std::unique_ptr to delete JNI local references.
class LocalRefDeleter {
public:
+ LocalRefDeleter() : env_(nullptr) {}
+
// Style guide violating implicit constructor so that the LocalRefDeleter
// is implicitly constructed from the second argument to ScopedLocalRef.
LocalRefDeleter(JNIEnv* env) : env_(env) {} // NOLINT(runtime/explicit)
@@ -43,7 +45,11 @@
}
// The delete operator.
- void operator()(jobject o) const { env_->DeleteLocalRef(o); }
+ void operator()(jobject object) const {
+ if (env_) {
+ env_->DeleteLocalRef(object);
+ }
+ }
private:
// The env_ stashed to use for deletion. Thread-local, don't share!
@@ -60,6 +66,6 @@
using ScopedLocalRef =
std::unique_ptr<typename std::remove_pointer<T>::type, LocalRefDeleter>;
-} // namespace libtextclassifier
+} // namespace libtextclassifier2
#endif // LIBTEXTCLASSIFIER_UTIL_JAVA_SCOPED_LOCAL_REF_H_
diff --git a/util/java/string_utils.cc b/util/java/string_utils.cc
new file mode 100644
index 0000000..ffd5b11
--- /dev/null
+++ b/util/java/string_utils.cc
@@ -0,0 +1,57 @@
+/*
+ * Copyright (C) 2017 The Android Open Source Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "util/java/string_utils.h"
+
+#include "util/base/logging.h"
+
+namespace libtextclassifier2 {
+
+bool JStringToUtf8String(JNIEnv* env, const jstring& jstr,
+ std::string* result) {
+ if (jstr == nullptr) {
+ *result = std::string();
+ return false;
+ }
+
+ jclass string_class = env->FindClass("java/lang/String");
+ if (!string_class) {
+ TC_LOG(ERROR) << "Can't find String class";
+ return false;
+ }
+
+ jmethodID get_bytes_id =
+ env->GetMethodID(string_class, "getBytes", "(Ljava/lang/String;)[B");
+
+ jstring encoding = env->NewStringUTF("UTF-8");
+ jbyteArray array = reinterpret_cast<jbyteArray>(
+ env->CallObjectMethod(jstr, get_bytes_id, encoding));
+
+ jbyte* const array_bytes = env->GetByteArrayElements(array, JNI_FALSE);
+ int length = env->GetArrayLength(array);
+
+ *result = std::string(reinterpret_cast<char*>(array_bytes), length);
+
+ // Release the array.
+ env->ReleaseByteArrayElements(array, array_bytes, JNI_ABORT);
+ env->DeleteLocalRef(array);
+ env->DeleteLocalRef(string_class);
+ env->DeleteLocalRef(encoding);
+
+ return true;
+}
+
+} // namespace libtextclassifier2
diff --git a/common/mock_functions.cc b/util/java/string_utils.h
similarity index 64%
rename from common/mock_functions.cc
rename to util/java/string_utils.h
index c661b70..6a85856 100644
--- a/common/mock_functions.cc
+++ b/util/java/string_utils.h
@@ -14,16 +14,16 @@
* limitations under the License.
*/
-#include "common/mock_functions.h"
+#ifndef LIBTEXTCLASSIFIER_UTIL_JAVA_STRING_UTILS_H_
+#define LIBTEXTCLASSIFIER_UTIL_JAVA_STRING_UTILS_H_
-#include "common/registry.h"
+#include <jni.h>
+#include <string>
-namespace libtextclassifier {
-namespace nlp_core {
+namespace libtextclassifier2 {
-TC_DEFINE_CLASS_REGISTRY_NAME("function", functions::Function);
+bool JStringToUtf8String(JNIEnv* env, const jstring& jstr, std::string* result);
-TC_DEFINE_CLASS_REGISTRY_NAME("int-function", functions::IntFunction);
+} // namespace libtextclassifier2
-} // namespace nlp_core
-} // namespace libtextclassifier
+#endif // LIBTEXTCLASSIFIER_UTIL_JAVA_STRING_UTILS_H_
diff --git a/common/fastexp.cc b/util/math/fastexp.cc
similarity index 93%
rename from common/fastexp.cc
rename to util/math/fastexp.cc
index 0376ad2..4bf8592 100644
--- a/common/fastexp.cc
+++ b/util/math/fastexp.cc
@@ -14,10 +14,9 @@
* limitations under the License.
*/
-#include "common/fastexp.h"
+#include "util/math/fastexp.h"
-namespace libtextclassifier {
-namespace nlp_core {
+namespace libtextclassifier2 {
const int FastMathClass::kBits;
const int FastMathClass::kMask1;
@@ -46,5 +45,4 @@
7940441, 8029106, 8118253, 8207884, 8298001}
};
-} // namespace nlp_core
-} // namespace libtextclassifier
+} // namespace libtextclassifier2
diff --git a/common/fastexp.h b/util/math/fastexp.h
similarity index 87%
rename from common/fastexp.h
rename to util/math/fastexp.h
index 1781b36..af7a08c 100644
--- a/common/fastexp.h
+++ b/util/math/fastexp.h
@@ -16,8 +16,8 @@
// Fast approximation for exp.
-#ifndef LIBTEXTCLASSIFIER_COMMON_FASTEXP_H_
-#define LIBTEXTCLASSIFIER_COMMON_FASTEXP_H_
+#ifndef LIBTEXTCLASSIFIER_UTIL_MATH_FASTEXP_H_
+#define LIBTEXTCLASSIFIER_UTIL_MATH_FASTEXP_H_
#include <cassert>
#include <cmath>
@@ -27,8 +27,7 @@
#include "util/base/integral_types.h"
#include "util/base/logging.h"
-namespace libtextclassifier {
-namespace nlp_core {
+namespace libtextclassifier2 {
class FastMathClass {
private:
@@ -64,7 +63,6 @@
inline float VeryFastExp2(float f) { return FastMathInstance.VeryFastExp2(f); }
inline float VeryFastExp(float f) { return FastMathInstance.VeryFastExp(f); }
-} // namespace nlp_core
-} // namespace libtextclassifier
+} // namespace libtextclassifier2
-#endif // LIBTEXTCLASSIFIER_COMMON_FASTEXP_H_
+#endif // LIBTEXTCLASSIFIER_UTIL_MATH_FASTEXP_H_
diff --git a/common/softmax.cc b/util/math/softmax.cc
similarity index 85%
rename from common/softmax.cc
rename to util/math/softmax.cc
index 3610de8..986787f 100644
--- a/common/softmax.cc
+++ b/util/math/softmax.cc
@@ -14,15 +14,14 @@
* limitations under the License.
*/
-#include "common/softmax.h"
+#include "util/math/softmax.h"
#include <limits>
-#include "common/fastexp.h"
#include "util/base/logging.h"
+#include "util/math/fastexp.h"
-namespace libtextclassifier {
-namespace nlp_core {
+namespace libtextclassifier2 {
float ComputeSoftmaxProbability(const std::vector<float> &scores, int label) {
if ((label < 0) || (label >= scores.size())) {
@@ -71,18 +70,24 @@
}
std::vector<float> ComputeSoftmax(const std::vector<float> &scores) {
+ return ComputeSoftmax(scores.data(), scores.size());
+}
+
+std::vector<float> ComputeSoftmax(const float *scores, int scores_size) {
std::vector<float> softmax;
std::vector<float> exp_scores;
- exp_scores.reserve(scores.size());
- softmax.reserve(scores.size());
+ exp_scores.reserve(scores_size);
+ softmax.reserve(scores_size);
// Find max value in "scores" vector and rescale to avoid overflows.
float max = std::numeric_limits<float>::min();
- for (const auto &score : scores) {
+ for (int i = 0; i < scores_size; ++i) {
+ const float score = scores[i];
if (score > max) max = score;
}
float denominator = 0;
- for (auto &score : scores) {
+ for (int i = 0; i < scores_size; ++i) {
+ const float score = scores[i];
// See comments above in ComputeSoftmaxProbability for the reasoning behind
// this approximation.
const float exp_score = score - max < -16.0f ? 0 : VeryFastExp(score - max);
@@ -90,11 +95,10 @@
denominator += exp_score;
}
- for (int i = 0; i < scores.size(); ++i) {
+ for (int i = 0; i < scores_size; ++i) {
softmax.push_back(exp_scores[i] / denominator);
}
return softmax;
}
-} // namespace nlp_core
-} // namespace libtextclassifier
+} // namespace libtextclassifier2
diff --git a/common/softmax.h b/util/math/softmax.h
similarity index 75%
rename from common/softmax.h
rename to util/math/softmax.h
index e1cc2d9..f70a9ab 100644
--- a/common/softmax.h
+++ b/util/math/softmax.h
@@ -14,13 +14,12 @@
* limitations under the License.
*/
-#ifndef LIBTEXTCLASSIFIER_COMMON_SOFTMAX_H_
-#define LIBTEXTCLASSIFIER_COMMON_SOFTMAX_H_
+#ifndef LIBTEXTCLASSIFIER_UTIL_MATH_SOFTMAX_H_
+#define LIBTEXTCLASSIFIER_UTIL_MATH_SOFTMAX_H_
#include <vector>
-namespace libtextclassifier {
-namespace nlp_core {
+namespace libtextclassifier2 {
// Computes probability of a softmax label. Parameter "scores" is the vector of
// softmax logits. Returns 0.0f if "label" is outside the range [0,
@@ -31,7 +30,9 @@
// "scores" is the vector of softmax logits.
std::vector<float> ComputeSoftmax(const std::vector<float> &scores);
-} // namespace nlp_core
-} // namespace libtextclassifier
+// Same as above but operates on an array of floats.
+std::vector<float> ComputeSoftmax(const float *scores, int scores_size);
-#endif // LIBTEXTCLASSIFIER_COMMON_SOFTMAX_H_
+} // namespace libtextclassifier2
+
+#endif // LIBTEXTCLASSIFIER_UTIL_MATH_SOFTMAX_H_
diff --git a/common/mmap.cc b/util/memory/mmap.cc
similarity index 96%
rename from common/mmap.cc
rename to util/memory/mmap.cc
index 6e15a84..6b0bdf2 100644
--- a/common/mmap.cc
+++ b/util/memory/mmap.cc
@@ -14,7 +14,7 @@
* limitations under the License.
*/
-#include "common/mmap.h"
+#include "util/memory/mmap.h"
#include <errno.h>
#include <fcntl.h>
@@ -27,8 +27,7 @@
#include "util/base/logging.h"
#include "util/base/macros.h"
-namespace libtextclassifier {
-namespace nlp_core {
+namespace libtextclassifier2 {
namespace {
inline std::string GetLastSystemError() { return std::string(strerror(errno)); }
@@ -133,5 +132,4 @@
return true;
}
-} // namespace nlp_core
-} // namespace libtextclassifier
+} // namespace libtextclassifier2
diff --git a/common/mmap.h b/util/memory/mmap.h
similarity index 94%
rename from common/mmap.h
rename to util/memory/mmap.h
index 69f7b4c..7d28b64 100644
--- a/common/mmap.h
+++ b/util/memory/mmap.h
@@ -14,8 +14,8 @@
* limitations under the License.
*/
-#ifndef LIBTEXTCLASSIFIER_COMMON_MMAP_H_
-#define LIBTEXTCLASSIFIER_COMMON_MMAP_H_
+#ifndef LIBTEXTCLASSIFIER_UTIL_MEMORY_MMAP_H_
+#define LIBTEXTCLASSIFIER_UTIL_MEMORY_MMAP_H_
#include <stddef.h>
@@ -24,8 +24,7 @@
#include "util/base/integral_types.h"
#include "util/strings/stringpiece.h"
-namespace libtextclassifier {
-namespace nlp_core {
+namespace libtextclassifier2 {
// Handle for a memory area where a file has been mmapped.
//
@@ -137,7 +136,6 @@
MmapHandle handle_;
};
-} // namespace nlp_core
-} // namespace libtextclassifier
+} // namespace libtextclassifier2
-#endif // LIBTEXTCLASSIFIER_COMMON_MMAP_H_
+#endif // LIBTEXTCLASSIFIER_UTIL_MEMORY_MMAP_H_
diff --git a/util/strings/numbers.cc b/util/strings/numbers.cc
index 4bd8b82..a89c0ef 100644
--- a/util/strings/numbers.cc
+++ b/util/strings/numbers.cc
@@ -22,7 +22,7 @@
#include <stdlib.h>
-namespace libtextclassifier {
+namespace libtextclassifier2 {
bool ParseInt32(const char *c_str, int32 *value) {
char *temp;
@@ -72,4 +72,4 @@
}
#endif // COMPILER_MSVC
-} // namespace libtextclassifier
+} // namespace libtextclassifier2
diff --git a/util/strings/numbers.h b/util/strings/numbers.h
index eda53bf..a2c8c6e 100644
--- a/util/strings/numbers.h
+++ b/util/strings/numbers.h
@@ -21,7 +21,7 @@
#include "util/base/integral_types.h"
-namespace libtextclassifier {
+namespace libtextclassifier2 {
// Parses an int32 from a C-style string.
//
@@ -47,7 +47,6 @@
// int types.
std::string IntToString(int64 input);
-
-} // namespace libtextclassifier
+} // namespace libtextclassifier2
#endif // LIBTEXTCLASSIFIER_UTIL_STRINGS_NUMBERS_H_
diff --git a/util/strings/numbers_test.cc b/util/strings/numbers_test.cc
index f3a3f27..1fdd78a 100644
--- a/util/strings/numbers_test.cc
+++ b/util/strings/numbers_test.cc
@@ -19,7 +19,7 @@
#include "util/base/integral_types.h"
#include "gtest/gtest.h"
-namespace libtextclassifier {
+namespace libtextclassifier2 {
namespace {
void TestParseInt32(const char *c_str, bool expected_parsing_success,
@@ -100,4 +100,4 @@
TestParseDouble("23.5a", false);
}
} // namespace
-} // namespace libtextclassifier
+} // namespace libtextclassifier2
diff --git a/util/strings/split.cc b/util/strings/split.cc
index 8d250bb..2c610ba 100644
--- a/util/strings/split.cc
+++ b/util/strings/split.cc
@@ -16,17 +16,17 @@
#include "util/strings/split.h"
-namespace libtextclassifier {
+namespace libtextclassifier2 {
namespace strings {
-std::vector<std::string> Split(const std::string &text, char delim) {
- std::vector<std::string> result;
+std::vector<StringPiece> Split(const StringPiece &text, char delim) {
+ std::vector<StringPiece> result;
int token_start = 0;
if (!text.empty()) {
for (size_t i = 0; i < text.size() + 1; i++) {
if ((i == text.size()) || (text[i] == delim)) {
result.push_back(
- std::string(text.data() + token_start, i - token_start));
+ StringPiece(text.data() + token_start, i - token_start));
token_start = i + 1;
}
}
@@ -35,4 +35,4 @@
}
} // namespace strings
-} // namespace libtextclassifier
+} // namespace libtextclassifier2
diff --git a/util/strings/split.h b/util/strings/split.h
index b661ede0..96f73fe 100644
--- a/util/strings/split.h
+++ b/util/strings/split.h
@@ -20,12 +20,14 @@
#include <string>
#include <vector>
-namespace libtextclassifier {
+#include "util/strings/stringpiece.h"
+
+namespace libtextclassifier2 {
namespace strings {
-std::vector<std::string> Split(const std::string &text, char delim);
+std::vector<StringPiece> Split(const StringPiece &text, char delim);
} // namespace strings
-} // namespace libtextclassifier
+} // namespace libtextclassifier2
#endif // LIBTEXTCLASSIFIER_UTIL_STRINGS_SPLIT_H_
diff --git a/util/strings/stringpiece.h b/util/strings/stringpiece.h
index 8c42d83..cd07848 100644
--- a/util/strings/stringpiece.h
+++ b/util/strings/stringpiece.h
@@ -21,7 +21,7 @@
#include <string>
-namespace libtextclassifier {
+namespace libtextclassifier2 {
// Read-only "view" of a piece of data. Does not own the underlying data.
class StringPiece {
@@ -51,6 +51,8 @@
size_t size() const { return size_; }
size_t length() const { return size_; }
+ bool empty() const { return size_ == 0; }
+
// Returns a std::string containing a copy of the underlying data.
std::string ToString() const {
return std::string(data(), size());
@@ -61,6 +63,6 @@
size_t size_;
};
-} // namespace libtextclassifier
+} // namespace libtextclassifier2
#endif // LIBTEXTCLASSIFIER_UTIL_STRINGS_STRINGPIECE_H_
diff --git a/util/strings/utf8.cc b/util/strings/utf8.cc
new file mode 100644
index 0000000..39dcb4e
--- /dev/null
+++ b/util/strings/utf8.cc
@@ -0,0 +1,42 @@
+/*
+ * Copyright (C) 2017 The Android Open Source Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "util/strings/utf8.h"
+
+namespace libtextclassifier2 {
+bool IsValidUTF8(const char *src, int size) {
+ for (int i = 0; i < size;) {
+ // Unexpected trail byte.
+ if (IsTrailByte(src[i])) {
+ return false;
+ }
+
+ const int num_codepoint_bytes = GetNumBytesForUTF8Char(&src[i]);
+ if (num_codepoint_bytes <= 0 || i + num_codepoint_bytes > size) {
+ return false;
+ }
+
+ // Check that remaining bytes in the codepoint are trailing bytes.
+ i++;
+ for (int k = 1; k < num_codepoint_bytes; k++, i++) {
+ if (!IsTrailByte(src[i])) {
+ return false;
+ }
+ }
+ }
+ return true;
+}
+} // namespace libtextclassifier2
diff --git a/util/strings/utf8.h b/util/strings/utf8.h
index 93c7fea..1e75da2 100644
--- a/util/strings/utf8.h
+++ b/util/strings/utf8.h
@@ -17,7 +17,7 @@
#ifndef LIBTEXTCLASSIFIER_UTIL_STRINGS_UTF8_H_
#define LIBTEXTCLASSIFIER_UTIL_STRINGS_UTF8_H_
-namespace libtextclassifier {
+namespace libtextclassifier2 {
// Returns the length (number of bytes) of the Unicode code point starting at
// src, based on inspecting just that one byte. Preconditions: src != NULL,
@@ -44,6 +44,9 @@
return static_cast<signed char>(x) < -0x40;
}
-} // namespace libtextclassifier
+// Returns true iff src points to a well-formed UTF-8 string.
+bool IsValidUTF8(const char *src, int size);
+
+} // namespace libtextclassifier2
#endif // LIBTEXTCLASSIFIER_UTIL_STRINGS_UTF8_H_
diff --git a/util/utf8/unicodetext.cc b/util/utf8/unicodetext.cc
index dbab1c8..2ef79e9 100644
--- a/util/utf8/unicodetext.cc
+++ b/util/utf8/unicodetext.cc
@@ -22,11 +22,21 @@
#include "util/strings/utf8.h"
-namespace libtextclassifier {
+namespace libtextclassifier2 {
// *************** Data representation **********
// Note: the copy constructor is undefined.
+UnicodeText::Repr& UnicodeText::Repr::operator=(Repr&& src) {
+ if (ours_ && data_) delete[] data_;
+ data_ = src.data_;
+ size_ = src.size_;
+ capacity_ = src.capacity_;
+ ours_ = src.ours_;
+ src.ours_ = false;
+ return *this;
+}
+
void UnicodeText::Repr::PointTo(const char* data, int size) {
if (ours_ && data_) delete[] data_; // If we owned the old buffer, free it.
data_ = const_cast<char*>(data);
@@ -89,6 +99,11 @@
UnicodeText::UnicodeText(const UnicodeText& src) { Copy(src); }
+UnicodeText& UnicodeText::operator=(UnicodeText&& src) {
+ this->repr_ = std::move(src.repr_);
+ return *this;
+}
+
UnicodeText& UnicodeText::Copy(const UnicodeText& src) {
repr_.Copy(src.repr_.data_, src.repr_.size_);
return *this;
@@ -109,9 +124,87 @@
return *this;
}
+const char* UnicodeText::data() const { return repr_.data_; }
+
+int UnicodeText::size_bytes() const { return repr_.size_; }
+
+namespace {
+
+enum {
+ RuneError = 0xFFFD, // Decoding error in UTF.
+ RuneMax = 0x10FFFF, // Maximum rune value.
+};
+
+int runetochar(const char32 rune, char* dest) {
+ // Convert to unsigned for range check.
+ uint32 c;
+
+ // 1 char 00-7F
+ c = rune;
+ if (c <= 0x7F) {
+ dest[0] = static_cast<char>(c);
+ return 1;
+ }
+
+ // 2 char 0080-07FF
+ if (c <= 0x07FF) {
+ dest[0] = 0xC0 | static_cast<char>(c >> 1 * 6);
+ dest[1] = 0x80 | (c & 0x3F);
+ return 2;
+ }
+
+ // Range check
+ if (c > RuneMax) {
+ c = RuneError;
+ }
+
+ // 3 char 0800-FFFF
+ if (c <= 0xFFFF) {
+ dest[0] = 0xE0 | static_cast<char>(c >> 2 * 6);
+ dest[1] = 0x80 | ((c >> 1 * 6) & 0x3F);
+ dest[2] = 0x80 | (c & 0x3F);
+ return 3;
+ }
+
+ // 4 char 10000-1FFFFF
+ dest[0] = 0xF0 | static_cast<char>(c >> 3 * 6);
+ dest[1] = 0x80 | ((c >> 2 * 6) & 0x3F);
+ dest[2] = 0x80 | ((c >> 1 * 6) & 0x3F);
+ dest[3] = 0x80 | (c & 0x3F);
+ return 4;
+}
+
+} // namespace
+
+UnicodeText& UnicodeText::AppendCodepoint(char32 ch) {
+ char str[4];
+ int char_len = runetochar(ch, str);
+ repr_.append(str, char_len);
+ return *this;
+}
+
void UnicodeText::clear() { repr_.clear(); }
-int UnicodeText::size() const { return std::distance(begin(), end()); }
+int UnicodeText::size_codepoints() const {
+ return std::distance(begin(), end());
+}
+
+bool UnicodeText::empty() const { return size_bytes() == 0; }
+
+bool UnicodeText::is_valid() const {
+ return IsValidUTF8(repr_.data_, repr_.size_);
+}
+
+bool UnicodeText::operator==(const UnicodeText& other) const {
+ if (repr_.size_ != other.repr_.size_) {
+ return false;
+ }
+ return memcmp(repr_.data_, other.repr_.data_, repr_.size_) == 0;
+}
+
+std::string UnicodeText::ToUTF8String() const {
+ return UTF8Substring(begin(), end());
+}
std::string UnicodeText::UTF8Substring(const const_iterator& first,
const const_iterator& last) {
@@ -191,8 +284,16 @@
return t;
}
+UnicodeText UTF8ToUnicodeText(const char* utf8_buf, bool do_copy) {
+ return UTF8ToUnicodeText(utf8_buf, strlen(utf8_buf), do_copy);
+}
+
UnicodeText UTF8ToUnicodeText(const std::string& str, bool do_copy) {
return UTF8ToUnicodeText(str.data(), str.size(), do_copy);
}
-} // namespace libtextclassifier
+UnicodeText UTF8ToUnicodeText(const std::string& str) {
+ return UTF8ToUnicodeText(str, /*do_copy=*/true);
+}
+
+} // namespace libtextclassifier2
diff --git a/util/utf8/unicodetext.h b/util/utf8/unicodetext.h
index 6a21058..ec08f53 100644
--- a/util/utf8/unicodetext.h
+++ b/util/utf8/unicodetext.h
@@ -23,7 +23,7 @@
#include "util/base/integral_types.h"
-namespace libtextclassifier {
+namespace libtextclassifier2 {
// ***************************** UnicodeText **************************
//
@@ -68,6 +68,7 @@
UnicodeText(); // Create an empty text.
UnicodeText(const UnicodeText& src);
+ UnicodeText& operator=(UnicodeText&& src);
~UnicodeText();
class const_iterator {
@@ -77,7 +78,7 @@
typedef std::input_iterator_tag iterator_category;
typedef char32 value_type;
typedef int difference_type;
- typedef void pointer; // (Not needed.)
+ typedef void pointer; // (Not needed.)
typedef const char32 reference; // (Needed for const_reverse_iterator)
// Iterators are default-constructible.
@@ -88,7 +89,7 @@
char32 operator*() const; // Dereference
- const_iterator& operator++(); // Advance (++iter)
+ const_iterator& operator++(); // Advance (++iter)
const_iterator operator++(int) { // (iter++)
const_iterator result(*this);
++*this;
@@ -132,14 +133,31 @@
private:
friend class UnicodeText;
- explicit const_iterator(const char *it) : it_(it) {}
+ explicit const_iterator(const char* it) : it_(it) {}
- const char *it_;
+ const char* it_;
};
const_iterator begin() const;
const_iterator end() const;
- int size() const; // the number of Unicode characters (codepoints)
+
+ // Gets pointer to the underlying utf8 data.
+ const char* data() const;
+
+ // Gets length (in bytes) of the underlying utf8 data.
+ int size_bytes() const;
+
+ // Computes length (in number of Unicode codepoints) of the underlying utf8
+ // data.
+ // NOTE: Complexity O(n).
+ int size_codepoints() const;
+
+ bool empty() const;
+
+ // Checks whether the underlying data is valid utf8 data.
+ bool is_valid() const;
+
+ bool operator==(const UnicodeText& other) const;
// x.PointToUTF8(buf,len) changes x so that it points to buf
// ("becomes an alias"). It does not take ownership or copy buf.
@@ -150,8 +168,10 @@
// Calling this may invalidate pointers to underlying data.
UnicodeText& AppendUTF8(const char* utf8, int len);
+ UnicodeText& AppendCodepoint(char32 ch);
void clear();
+ std::string ToUTF8String() const;
static std::string UTF8Substring(const const_iterator& first,
const const_iterator& last);
@@ -166,6 +186,7 @@
bool ours_; // Do we own data_?
Repr() : data_(nullptr), size_(0), capacity_(0), ours_(true) {}
+ Repr& operator=(Repr&& src);
~Repr() {
if (ours_) delete[] data_;
}
@@ -176,7 +197,6 @@
void append(const char* bytes, int byte_length);
void Copy(const char* data, int size);
- void TakeOwnershipOf(char* data, int size, int capacity);
void PointTo(const char* data, int size);
private:
@@ -190,9 +210,15 @@
typedef std::pair<UnicodeText::const_iterator, UnicodeText::const_iterator>
UnicodeTextRange;
+// NOTE: The following are needed to avoid implicit conversion from char* to
+// std::string, or from ::string to std::string, because if this happens it
+// often results in invalid memory access to a temporary object created during
+// such conversion (if do_copy == false).
UnicodeText UTF8ToUnicodeText(const char* utf8_buf, int len, bool do_copy);
+UnicodeText UTF8ToUnicodeText(const char* utf8_buf, bool do_copy);
UnicodeText UTF8ToUnicodeText(const std::string& str, bool do_copy);
+UnicodeText UTF8ToUnicodeText(const std::string& str);
-} // namespace libtextclassifier
+} // namespace libtextclassifier2
#endif // LIBTEXTCLASSIFIER_UTIL_UTF8_UNICODETEXT_H_
diff --git a/util/utf8/unicodetext_test.cc b/util/utf8/unicodetext_test.cc
new file mode 100644
index 0000000..9ec7621
--- /dev/null
+++ b/util/utf8/unicodetext_test.cc
@@ -0,0 +1,189 @@
+/*
+ * Copyright (C) 2017 The Android Open Source Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "util/utf8/unicodetext.h"
+
+#include "gtest/gtest.h"
+
+namespace libtextclassifier2 {
+namespace {
+
+class UnicodeTextTest : public testing::Test {
+ protected:
+ UnicodeTextTest() : empty_text_() {
+ text_.AppendCodepoint(0x1C0);
+ text_.AppendCodepoint(0x4E8C);
+ text_.AppendCodepoint(0xD7DB);
+ text_.AppendCodepoint(0x34);
+ text_.AppendCodepoint(0x1D11E);
+ }
+
+ UnicodeText empty_text_;
+ UnicodeText text_;
+};
+
+// Tests for our modifications of UnicodeText.
+TEST(UnicodeTextTest, Custom) {
+ UnicodeText text = UTF8ToUnicodeText("1234😋hello", /*do_copy=*/false);
+ EXPECT_EQ(text.ToUTF8String(), "1234😋hello");
+ EXPECT_EQ(text.size_codepoints(), 10);
+ EXPECT_EQ(text.size_bytes(), 13);
+
+ auto it_begin = text.begin();
+ std::advance(it_begin, 4);
+ auto it_end = text.begin();
+ std::advance(it_end, 6);
+ EXPECT_EQ(text.UTF8Substring(it_begin, it_end), "😋h");
+}
+
+TEST(UnicodeTextTest, Ownership) {
+ const std::string src = "\u304A\u00B0\u106B";
+
+ UnicodeText alias;
+ alias.PointToUTF8(src.data(), src.size());
+ EXPECT_EQ(alias.data(), src.data());
+ UnicodeText::const_iterator it = alias.begin();
+ EXPECT_EQ(*it++, 0x304A);
+ EXPECT_EQ(*it++, 0x00B0);
+ EXPECT_EQ(*it++, 0x106B);
+ EXPECT_EQ(it, alias.end());
+
+ UnicodeText t = alias; // Copy initialization copies the data.
+ EXPECT_NE(t.data(), alias.data());
+}
+
+TEST(UnicodeTextTest, Validation) {
+ EXPECT_TRUE(UTF8ToUnicodeText("1234😋hello", /*do_copy=*/false).is_valid());
+ EXPECT_TRUE(
+ UTF8ToUnicodeText("\u304A\u00B0\u106B", /*do_copy=*/false).is_valid());
+ EXPECT_TRUE(
+ UTF8ToUnicodeText("this is a test😋😋😋", /*do_copy=*/false).is_valid());
+ EXPECT_TRUE(
+ UTF8ToUnicodeText("\xf0\x9f\x98\x8b", /*do_copy=*/false).is_valid());
+ // Too short (string is too short).
+ EXPECT_FALSE(UTF8ToUnicodeText("\xf0\x9f", /*do_copy=*/false).is_valid());
+ // Too long (too many trailing bytes).
+ EXPECT_FALSE(
+ UTF8ToUnicodeText("\xf0\x9f\x98\x8b\x8b", /*do_copy=*/false).is_valid());
+ // Too short (too few trailing bytes).
+ EXPECT_FALSE(
+ UTF8ToUnicodeText("\xf0\x9f\x98\x61\x61", /*do_copy=*/false).is_valid());
+ // Invalid with context.
+ EXPECT_FALSE(
+ UTF8ToUnicodeText("hello \xf0\x9f\x98\x61\x61 world1", /*do_copy=*/false)
+ .is_valid());
+}
+
+class IteratorTest : public UnicodeTextTest {};
+
+TEST_F(IteratorTest, Iterates) {
+ UnicodeText::const_iterator iter = text_.begin();
+ EXPECT_EQ(0x1C0, *iter);
+ EXPECT_EQ(&iter, &++iter); // operator++ returns *this.
+ EXPECT_EQ(0x4E8C, *iter++);
+ EXPECT_EQ(0xD7DB, *iter);
+ // Make sure you can dereference more than once.
+ EXPECT_EQ(0xD7DB, *iter);
+ EXPECT_EQ(0x34, *++iter);
+ EXPECT_EQ(0x1D11E, *++iter);
+ ASSERT_TRUE(iter != text_.end());
+ iter++;
+ EXPECT_TRUE(iter == text_.end());
+}
+
+TEST_F(IteratorTest, MultiPass) {
+ // Also tests Default Constructible and Assignable.
+ UnicodeText::const_iterator i1, i2;
+ i1 = text_.begin();
+ i2 = i1;
+ EXPECT_EQ(0x4E8C, *++i1);
+ EXPECT_TRUE(i1 != i2);
+ EXPECT_EQ(0x1C0, *i2);
+ ++i2;
+ EXPECT_TRUE(i1 == i2);
+ EXPECT_EQ(0x4E8C, *i2);
+}
+
+TEST_F(IteratorTest, ReverseIterates) {
+ UnicodeText::const_iterator iter = text_.end();
+ EXPECT_TRUE(iter == text_.end());
+ iter--;
+ ASSERT_TRUE(iter != text_.end());
+ EXPECT_EQ(0x1D11E, *iter--);
+ EXPECT_EQ(0x34, *iter);
+ EXPECT_EQ(0xD7DB, *--iter);
+ // Make sure you can dereference more than once.
+ EXPECT_EQ(0xD7DB, *iter);
+ --iter;
+ EXPECT_EQ(0x4E8C, *iter--);
+ EXPECT_EQ(0x1C0, *iter);
+ EXPECT_TRUE(iter == text_.begin());
+}
+
+TEST_F(IteratorTest, Comparable) {
+ UnicodeText::const_iterator i1, i2;
+ i1 = text_.begin();
+ i2 = i1;
+ ++i2;
+
+ EXPECT_TRUE(i1 < i2);
+ EXPECT_TRUE(text_.begin() <= i1);
+ EXPECT_FALSE(i1 >= i2);
+ EXPECT_FALSE(i1 > text_.end());
+}
+
+TEST_F(IteratorTest, Advance) {
+ UnicodeText::const_iterator iter = text_.begin();
+ EXPECT_EQ(0x1C0, *iter);
+ std::advance(iter, 4);
+ EXPECT_EQ(0x1D11E, *iter);
+ ++iter;
+ EXPECT_TRUE(iter == text_.end());
+}
+
+TEST_F(IteratorTest, Distance) {
+ UnicodeText::const_iterator iter = text_.begin();
+ EXPECT_EQ(0, std::distance(text_.begin(), iter));
+ EXPECT_EQ(5, std::distance(iter, text_.end()));
+ ++iter;
+ ++iter;
+ EXPECT_EQ(2, std::distance(text_.begin(), iter));
+ EXPECT_EQ(3, std::distance(iter, text_.end()));
+ ++iter;
+ ++iter;
+ EXPECT_EQ(4, std::distance(text_.begin(), iter));
+ ++iter;
+ EXPECT_EQ(0, std::distance(iter, text_.end()));
+}
+
+class OperatorTest : public UnicodeTextTest {};
+
+TEST_F(OperatorTest, Clear) {
+ UnicodeText empty_text(UTF8ToUnicodeText("", /*do_copy=*/false));
+ EXPECT_FALSE(text_ == empty_text);
+ text_.clear();
+ EXPECT_TRUE(text_ == empty_text);
+}
+
+TEST_F(OperatorTest, Empty) {
+ EXPECT_TRUE(empty_text_.empty());
+ EXPECT_FALSE(text_.empty());
+ text_.clear();
+ EXPECT_TRUE(text_.empty());
+}
+
+} // namespace
+} // namespace libtextclassifier2
diff --git a/util/utf8/unilib-icu.cc b/util/utf8/unilib-icu.cc
new file mode 100644
index 0000000..9e9ce19
--- /dev/null
+++ b/util/utf8/unilib-icu.cc
@@ -0,0 +1,293 @@
+/*
+ * Copyright (C) 2017 The Android Open Source Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "util/utf8/unilib-icu.h"
+
+#include <utility>
+
+namespace libtextclassifier2 {
+
+bool UniLib::ParseInt32(const UnicodeText& text, int* result) const {
+ UErrorCode status = U_ZERO_ERROR;
+ UNumberFormat* format_alias =
+ unum_open(UNUM_DECIMAL, nullptr, 0, "en_US_POSIX", nullptr, &status);
+ if (U_FAILURE(status)) {
+ return false;
+ }
+ icu::UnicodeString utf8_string = icu::UnicodeString::fromUTF8(
+ icu::StringPiece(text.data(), text.size_bytes()));
+ int parse_index = 0;
+ const int32 integer = unum_parse(format_alias, utf8_string.getBuffer(),
+ utf8_string.length(), &parse_index, &status);
+ *result = integer;
+ unum_close(format_alias);
+ if (U_FAILURE(status) || parse_index != utf8_string.length()) {
+ return false;
+ }
+ return true;
+}
+
+bool UniLib::IsOpeningBracket(char32 codepoint) const {
+ return u_getIntPropertyValue(codepoint, UCHAR_BIDI_PAIRED_BRACKET_TYPE) ==
+ U_BPT_OPEN;
+}
+
+bool UniLib::IsClosingBracket(char32 codepoint) const {
+ return u_getIntPropertyValue(codepoint, UCHAR_BIDI_PAIRED_BRACKET_TYPE) ==
+ U_BPT_CLOSE;
+}
+
+bool UniLib::IsWhitespace(char32 codepoint) const {
+ return u_isWhitespace(codepoint);
+}
+
+bool UniLib::IsDigit(char32 codepoint) const { return u_isdigit(codepoint); }
+
+bool UniLib::IsUpper(char32 codepoint) const { return u_isupper(codepoint); }
+
+char32 UniLib::ToLower(char32 codepoint) const { return u_tolower(codepoint); }
+
+char32 UniLib::GetPairedBracket(char32 codepoint) const {
+ return u_getBidiPairedBracket(codepoint);
+}
+
+UniLib::RegexMatcher::RegexMatcher(icu::RegexPattern* pattern,
+ icu::UnicodeString text)
+ : text_(std::move(text)),
+ last_find_offset_(0),
+ last_find_offset_codepoints_(0),
+ last_find_offset_dirty_(true) {
+ UErrorCode status = U_ZERO_ERROR;
+ matcher_.reset(pattern->matcher(text_, status));
+ if (U_FAILURE(status)) {
+ matcher_.reset(nullptr);
+ }
+}
+
+std::unique_ptr<UniLib::RegexMatcher> UniLib::RegexPattern::Matcher(
+ const UnicodeText& input) const {
+ return std::unique_ptr<UniLib::RegexMatcher>(new UniLib::RegexMatcher(
+ pattern_.get(), icu::UnicodeString::fromUTF8(
+ icu::StringPiece(input.data(), input.size_bytes()))));
+}
+
+constexpr int UniLib::RegexMatcher::kError;
+constexpr int UniLib::RegexMatcher::kNoError;
+
+bool UniLib::RegexMatcher::Matches(int* status) const {
+ if (!matcher_) {
+ *status = kError;
+ return false;
+ }
+
+ UErrorCode icu_status = U_ZERO_ERROR;
+ const bool result = matcher_->matches(/*startIndex=*/0, icu_status);
+ if (U_FAILURE(icu_status)) {
+ *status = kError;
+ return false;
+ }
+ *status = kNoError;
+ return result;
+}
+
+bool UniLib::RegexMatcher::ApproximatelyMatches(int* status) {
+ if (!matcher_) {
+ *status = kError;
+ return false;
+ }
+
+ matcher_->reset();
+ *status = kNoError;
+ if (!Find(status) || *status != kNoError) {
+ return false;
+ }
+ const int found_start = Start(status);
+ if (*status != kNoError) {
+ return false;
+ }
+ const int found_end = End(status);
+ if (*status != kNoError) {
+ return false;
+ }
+ if (found_start != 0 || found_end != text_.countChar32()) {
+ return false;
+ }
+ return true;
+}
+
+bool UniLib::RegexMatcher::UpdateLastFindOffset() const {
+ if (!last_find_offset_dirty_) {
+ return true;
+ }
+
+ // Update the position of the match.
+ UErrorCode icu_status = U_ZERO_ERROR;
+ const int find_offset = matcher_->start(0, icu_status);
+ if (U_FAILURE(icu_status)) {
+ return false;
+ }
+ last_find_offset_codepoints_ +=
+ text_.countChar32(last_find_offset_, find_offset - last_find_offset_);
+ last_find_offset_ = find_offset;
+ last_find_offset_dirty_ = false;
+
+ return true;
+}
+
+bool UniLib::RegexMatcher::Find(int* status) {
+ if (!matcher_) {
+ *status = kError;
+ return false;
+ }
+ UErrorCode icu_status = U_ZERO_ERROR;
+ const bool result = matcher_->find(icu_status);
+ if (U_FAILURE(icu_status)) {
+ *status = kError;
+ return false;
+ }
+
+ last_find_offset_dirty_ = true;
+ *status = kNoError;
+ return result;
+}
+
+int UniLib::RegexMatcher::Start(int* status) const {
+ return Start(/*group_idx=*/0, status);
+}
+
+int UniLib::RegexMatcher::Start(int group_idx, int* status) const {
+ if (!matcher_ || !UpdateLastFindOffset()) {
+ *status = kError;
+ return kError;
+ }
+
+ UErrorCode icu_status = U_ZERO_ERROR;
+ const int result = matcher_->start(group_idx, icu_status);
+ if (U_FAILURE(icu_status)) {
+ *status = kError;
+ return kError;
+ }
+ *status = kNoError;
+
+ // If the group didn't participate in the match the result is -1 and is
+ // incompatible with the caching logic bellow.
+ if (result == -1) {
+ return -1;
+ }
+
+ return last_find_offset_codepoints_ +
+ text_.countChar32(/*start=*/last_find_offset_,
+ /*length=*/result - last_find_offset_);
+}
+
+int UniLib::RegexMatcher::End(int* status) const {
+ return End(/*group_idx=*/0, status);
+}
+
+int UniLib::RegexMatcher::End(int group_idx, int* status) const {
+ if (!matcher_ || !UpdateLastFindOffset()) {
+ *status = kError;
+ return kError;
+ }
+ UErrorCode icu_status = U_ZERO_ERROR;
+ const int result = matcher_->end(group_idx, icu_status);
+ if (U_FAILURE(icu_status)) {
+ *status = kError;
+ return kError;
+ }
+ *status = kNoError;
+
+ // If the group didn't participate in the match the result is -1 and is
+ // incompatible with the caching logic bellow.
+ if (result == -1) {
+ return -1;
+ }
+
+ return last_find_offset_codepoints_ +
+ text_.countChar32(/*start=*/last_find_offset_,
+ /*length=*/result - last_find_offset_);
+}
+
+UnicodeText UniLib::RegexMatcher::Group(int* status) const {
+ return Group(/*group_idx=*/0, status);
+}
+
+UnicodeText UniLib::RegexMatcher::Group(int group_idx, int* status) const {
+ if (!matcher_) {
+ *status = kError;
+ return UTF8ToUnicodeText("", /*do_copy=*/false);
+ }
+ std::string result = "";
+ UErrorCode icu_status = U_ZERO_ERROR;
+ const icu::UnicodeString result_icu = matcher_->group(group_idx, icu_status);
+ if (U_FAILURE(icu_status)) {
+ *status = kError;
+ return UTF8ToUnicodeText("", /*do_copy=*/false);
+ }
+ result_icu.toUTF8String(result);
+ *status = kNoError;
+ return UTF8ToUnicodeText(result, /*do_copy=*/true);
+}
+
+constexpr int UniLib::BreakIterator::kDone;
+
+UniLib::BreakIterator::BreakIterator(const UnicodeText& text)
+ : text_(icu::UnicodeString::fromUTF8(
+ icu::StringPiece(text.data(), text.size_bytes()))),
+ last_break_index_(0),
+ last_unicode_index_(0) {
+ icu::ErrorCode status;
+ break_iterator_.reset(
+ icu::BreakIterator::createWordInstance(icu::Locale("en"), status));
+ if (!status.isSuccess()) {
+ break_iterator_.reset();
+ return;
+ }
+ break_iterator_->setText(text_);
+}
+
+int UniLib::BreakIterator::Next() {
+ const int break_index = break_iterator_->next();
+ if (break_index == icu::BreakIterator::DONE) {
+ return BreakIterator::kDone;
+ }
+ last_unicode_index_ +=
+ text_.countChar32(last_break_index_, break_index - last_break_index_);
+ last_break_index_ = break_index;
+ return last_unicode_index_;
+}
+
+std::unique_ptr<UniLib::RegexPattern> UniLib::CreateRegexPattern(
+ const UnicodeText& regex) const {
+ UErrorCode status = U_ZERO_ERROR;
+ std::unique_ptr<icu::RegexPattern> pattern(
+ icu::RegexPattern::compile(icu::UnicodeString::fromUTF8(icu::StringPiece(
+ regex.data(), regex.size_bytes())),
+ /*flags=*/UREGEX_MULTILINE, status));
+ if (U_FAILURE(status) || !pattern) {
+ return nullptr;
+ }
+ return std::unique_ptr<UniLib::RegexPattern>(
+ new UniLib::RegexPattern(std::move(pattern)));
+}
+
+std::unique_ptr<UniLib::BreakIterator> UniLib::CreateBreakIterator(
+ const UnicodeText& text) const {
+ return std::unique_ptr<UniLib::BreakIterator>(
+ new UniLib::BreakIterator(text));
+}
+
+} // namespace libtextclassifier2
diff --git a/util/utf8/unilib-icu.h b/util/utf8/unilib-icu.h
new file mode 100644
index 0000000..8983756
--- /dev/null
+++ b/util/utf8/unilib-icu.h
@@ -0,0 +1,155 @@
+/*
+ * Copyright (C) 2017 The Android Open Source Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+// UniLib implementation with the help of ICU. UniLib is basically a wrapper
+// around the ICU functionality.
+
+#ifndef LIBTEXTCLASSIFIER_UTIL_UTF8_UNILIB_ICU_H_
+#define LIBTEXTCLASSIFIER_UTIL_UTF8_UNILIB_ICU_H_
+
+#include <memory>
+
+#include "util/base/integral_types.h"
+#include "util/utf8/unicodetext.h"
+#include "unicode/brkiter.h"
+#include "unicode/errorcode.h"
+#include "unicode/regex.h"
+#include "unicode/uchar.h"
+#include "unicode/unum.h"
+
+namespace libtextclassifier2 {
+
+class UniLib {
+ public:
+ bool ParseInt32(const UnicodeText& text, int* result) const;
+ bool IsOpeningBracket(char32 codepoint) const;
+ bool IsClosingBracket(char32 codepoint) const;
+ bool IsWhitespace(char32 codepoint) const;
+ bool IsDigit(char32 codepoint) const;
+ bool IsUpper(char32 codepoint) const;
+
+ char32 ToLower(char32 codepoint) const;
+ char32 GetPairedBracket(char32 codepoint) const;
+
+ // Forward declaration for friend.
+ class RegexPattern;
+
+ class RegexMatcher {
+ public:
+ static constexpr int kError = -1;
+ static constexpr int kNoError = 0;
+
+ // Checks whether the input text matches the pattern exactly.
+ bool Matches(int* status) const;
+
+ // Approximate Matches() implementation implemented using Find(). It uses
+ // the first Find() result and then checks that it spans the whole input.
+ // NOTE: Unlike Matches() it can result in false negatives.
+ // NOTE: Resets the matcher, so the current Find() state will be lost.
+ bool ApproximatelyMatches(int* status);
+
+ // Finds occurrences of the pattern in the input text.
+ // Can be called repeatedly to find all occurences. A call will update
+ // internal state, so that 'Start', 'End' and 'Group' can be called to get
+ // information about the match.
+ // NOTE: Any call to ApproximatelyMatches() in between Find() calls will
+ // modify the state.
+ bool Find(int* status);
+
+ // Gets the start offset of the last match (from 'Find').
+ // Sets status to 'kError' if 'Find'
+ // was not called previously.
+ int Start(int* status) const;
+
+ // Gets the start offset of the specified group of the last match.
+ // (from 'Find').
+ // Sets status to 'kError' if an invalid group was specified or if 'Find'
+ // was not called previously.
+ int Start(int group_idx, int* status) const;
+
+ // Gets the end offset of the last match (from 'Find').
+ // Sets status to 'kError' if 'Find'
+ // was not called previously.
+ int End(int* status) const;
+
+ // Gets the end offset of the specified group of the last match.
+ // (from 'Find').
+ // Sets status to 'kError' if an invalid group was specified or if 'Find'
+ // was not called previously.
+ int End(int group_idx, int* status) const;
+
+ // Gets the text of the last match (from 'Find').
+ // Sets status to 'kError' if 'Find' was not called previously.
+ UnicodeText Group(int* status) const;
+
+ // Gets the text of the specified group of the last match (from 'Find').
+ // Sets status to 'kError' if an invalid group was specified or if 'Find'
+ // was not called previously.
+ UnicodeText Group(int group_idx, int* status) const;
+
+ protected:
+ friend class RegexPattern;
+ explicit RegexMatcher(icu::RegexPattern* pattern, icu::UnicodeString text);
+
+ private:
+ bool UpdateLastFindOffset() const;
+
+ std::unique_ptr<icu::RegexMatcher> matcher_;
+ icu::UnicodeString text_;
+ mutable int last_find_offset_;
+ mutable int last_find_offset_codepoints_;
+ mutable bool last_find_offset_dirty_;
+ };
+
+ class RegexPattern {
+ public:
+ std::unique_ptr<RegexMatcher> Matcher(const UnicodeText& input) const;
+
+ protected:
+ friend class UniLib;
+ explicit RegexPattern(std::unique_ptr<icu::RegexPattern> pattern)
+ : pattern_(std::move(pattern)) {}
+
+ private:
+ std::unique_ptr<icu::RegexPattern> pattern_;
+ };
+
+ class BreakIterator {
+ public:
+ int Next();
+
+ static constexpr int kDone = -1;
+
+ protected:
+ friend class UniLib;
+ explicit BreakIterator(const UnicodeText& text);
+
+ private:
+ std::unique_ptr<icu::BreakIterator> break_iterator_;
+ icu::UnicodeString text_;
+ int last_break_index_;
+ int last_unicode_index_;
+ };
+
+ std::unique_ptr<RegexPattern> CreateRegexPattern(
+ const UnicodeText& regex) const;
+ std::unique_ptr<BreakIterator> CreateBreakIterator(
+ const UnicodeText& text) const;
+};
+
+} // namespace libtextclassifier2
+
+#endif // LIBTEXTCLASSIFIER_UTIL_UTF8_UNILIB_ICU_H_
diff --git a/common/config.h b/util/utf8/unilib.h
similarity index 67%
copy from common/config.h
copy to util/utf8/unilib.h
index b883e95..29b4575 100644
--- a/common/config.h
+++ b/util/utf8/unilib.h
@@ -14,16 +14,10 @@
* limitations under the License.
*/
-#ifndef LIBTEXTCLASSIFIER_COMMON_CONFIG_H_
-#define LIBTEXTCLASSIFIER_COMMON_CONFIG_H_
+#ifndef LIBTEXTCLASSIFIER_UTIL_UTF8_UNILIB_H_
+#define LIBTEXTCLASSIFIER_UTIL_UTF8_UNILIB_H_
-#ifndef PORTABLE_SAFT_MOBILE
-#if defined(__ANDROID__) || defined(__APPLE__)
-#define PORTABLE_SAFT_MOBILE 1
-#else
-#define PORTABLE_SAFT_MOBILE 0
-#endif
+#include "util/utf8/unilib-icu.h"
+#define CREATE_UNILIB_FOR_TESTING const UniLib unilib;
-#endif
-
-#endif // LIBTEXTCLASSIFIER_COMMON_CONFIG_H_
+#endif // LIBTEXTCLASSIFIER_UTIL_UTF8_UNILIB_H_
diff --git a/util/utf8/unilib_test.cc b/util/utf8/unilib_test.cc
new file mode 100644
index 0000000..13b1347
--- /dev/null
+++ b/util/utf8/unilib_test.cc
@@ -0,0 +1,232 @@
+/*
+ * Copyright (C) 2017 The Android Open Source Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "util/utf8/unilib.h"
+
+#include "util/base/logging.h"
+#include "util/utf8/unicodetext.h"
+#include "gmock/gmock.h"
+#include "gtest/gtest.h"
+
+namespace libtextclassifier2 {
+namespace {
+
+using ::testing::ElementsAre;
+
+TEST(UniLibTest, CharacterClassesAscii) {
+ CREATE_UNILIB_FOR_TESTING;
+ EXPECT_TRUE(unilib.IsOpeningBracket('('));
+ EXPECT_TRUE(unilib.IsClosingBracket(')'));
+ EXPECT_FALSE(unilib.IsWhitespace(')'));
+ EXPECT_TRUE(unilib.IsWhitespace(' '));
+ EXPECT_FALSE(unilib.IsDigit(')'));
+ EXPECT_TRUE(unilib.IsDigit('0'));
+ EXPECT_TRUE(unilib.IsDigit('9'));
+ EXPECT_FALSE(unilib.IsUpper(')'));
+ EXPECT_TRUE(unilib.IsUpper('A'));
+ EXPECT_TRUE(unilib.IsUpper('Z'));
+ EXPECT_EQ(unilib.ToLower('A'), 'a');
+ EXPECT_EQ(unilib.ToLower('Z'), 'z');
+ EXPECT_EQ(unilib.ToLower(')'), ')');
+ EXPECT_EQ(unilib.GetPairedBracket(')'), '(');
+ EXPECT_EQ(unilib.GetPairedBracket('}'), '{');
+}
+
+#ifndef LIBTEXTCLASSIFIER_UNILIB_DUMMY
+TEST(UniLibTest, CharacterClassesUnicode) {
+ CREATE_UNILIB_FOR_TESTING;
+ EXPECT_TRUE(unilib.IsOpeningBracket(0x0F3C)); // TIBET ANG KHANG GYON
+ EXPECT_TRUE(unilib.IsClosingBracket(0x0F3D)); // TIBET ANG KHANG GYAS
+ EXPECT_FALSE(unilib.IsWhitespace(0x23F0)); // ALARM CLOCK
+ EXPECT_TRUE(unilib.IsWhitespace(0x2003)); // EM SPACE
+ EXPECT_FALSE(unilib.IsDigit(0xA619)); // VAI SYMBOL JONG
+ EXPECT_TRUE(unilib.IsDigit(0xA620)); // VAI DIGIT ZERO
+ EXPECT_TRUE(unilib.IsDigit(0xA629)); // VAI DIGIT NINE
+ EXPECT_FALSE(unilib.IsDigit(0xA62A)); // VAI SYLLABLE NDOLE MA
+ EXPECT_FALSE(unilib.IsUpper(0x0211)); // SMALL R WITH DOUBLE GRAVE
+ EXPECT_TRUE(unilib.IsUpper(0x0212)); // CAPITAL R WITH DOUBLE GRAVE
+ EXPECT_TRUE(unilib.IsUpper(0x0391)); // GREEK CAPITAL ALPHA
+ EXPECT_TRUE(unilib.IsUpper(0x03AB)); // GREEK CAPITAL UPSILON W DIAL
+ EXPECT_FALSE(unilib.IsUpper(0x03AC)); // GREEK SMALL ALPHA WITH TONOS
+ EXPECT_EQ(unilib.ToLower(0x0391), 0x03B1); // GREEK ALPHA
+ EXPECT_EQ(unilib.ToLower(0x03AB), 0x03CB); // GREEK UPSILON WITH DIALYTIKA
+ EXPECT_EQ(unilib.ToLower(0x03C0), 0x03C0); // GREEK SMALL PI
+
+ EXPECT_EQ(unilib.GetPairedBracket(0x0F3C), 0x0F3D);
+ EXPECT_EQ(unilib.GetPairedBracket(0x0F3D), 0x0F3C);
+}
+#endif // ndef LIBTEXTCLASSIFIER_UNILIB_DUMMY
+
+TEST(UniLibTest, RegexInterface) {
+ CREATE_UNILIB_FOR_TESTING;
+ const UnicodeText regex_pattern =
+ UTF8ToUnicodeText("[0-9]+", /*do_copy=*/true);
+ std::unique_ptr<UniLib::RegexPattern> pattern =
+ unilib.CreateRegexPattern(regex_pattern);
+ const UnicodeText input = UTF8ToUnicodeText("hello 0123", /*do_copy=*/false);
+ int status;
+ std::unique_ptr<UniLib::RegexMatcher> matcher = pattern->Matcher(input);
+ TC_LOG(INFO) << matcher->Matches(&status);
+ TC_LOG(INFO) << matcher->Find(&status);
+ TC_LOG(INFO) << matcher->Start(0, &status);
+ TC_LOG(INFO) << matcher->End(0, &status);
+ TC_LOG(INFO) << matcher->Group(0, &status).size_codepoints();
+}
+
+#ifdef LIBTEXTCLASSIFIER_UNILIB_ICU
+TEST(UniLibTest, Regex) {
+ CREATE_UNILIB_FOR_TESTING;
+
+ // The smiley face is a 4-byte UTF8 codepoint 0x1F60B, and it's important to
+ // test the regex functionality with it to verify we are handling the indices
+ // correctly.
+ const UnicodeText regex_pattern =
+ UTF8ToUnicodeText("[0-9]+😋", /*do_copy=*/false);
+ std::unique_ptr<UniLib::RegexPattern> pattern =
+ unilib.CreateRegexPattern(regex_pattern);
+ int status;
+ std::unique_ptr<UniLib::RegexMatcher> matcher;
+
+ matcher = pattern->Matcher(UTF8ToUnicodeText("0123😋", /*do_copy=*/false));
+ EXPECT_TRUE(matcher->Matches(&status));
+ EXPECT_TRUE(matcher->ApproximatelyMatches(&status));
+ EXPECT_EQ(status, UniLib::RegexMatcher::kNoError);
+ EXPECT_TRUE(matcher->Matches(&status)); // Check that the state is reset.
+ EXPECT_TRUE(matcher->ApproximatelyMatches(&status));
+ EXPECT_EQ(status, UniLib::RegexMatcher::kNoError);
+
+ matcher = pattern->Matcher(
+ UTF8ToUnicodeText("hello😋😋 0123😋 world", /*do_copy=*/false));
+ EXPECT_FALSE(matcher->Matches(&status));
+ EXPECT_FALSE(matcher->ApproximatelyMatches(&status));
+ EXPECT_EQ(status, UniLib::RegexMatcher::kNoError);
+
+ matcher = pattern->Matcher(
+ UTF8ToUnicodeText("hello😋😋 0123😋 world", /*do_copy=*/false));
+ EXPECT_TRUE(matcher->Find(&status));
+ EXPECT_EQ(status, UniLib::RegexMatcher::kNoError);
+ EXPECT_EQ(matcher->Start(0, &status), 8);
+ EXPECT_EQ(status, UniLib::RegexMatcher::kNoError);
+ EXPECT_EQ(matcher->End(0, &status), 13);
+ EXPECT_EQ(status, UniLib::RegexMatcher::kNoError);
+ EXPECT_EQ(matcher->Group(0, &status).ToUTF8String(), "0123😋");
+ EXPECT_EQ(status, UniLib::RegexMatcher::kNoError);
+}
+#endif // LIBTEXTCLASSIFIER_UNILIB_ICU
+
+#ifdef LIBTEXTCLASSIFIER_UNILIB_ICU
+TEST(UniLibTest, RegexGroups) {
+ CREATE_UNILIB_FOR_TESTING;
+
+ // The smiley face is a 4-byte UTF8 codepoint 0x1F60B, and it's important to
+ // test the regex functionality with it to verify we are handling the indices
+ // correctly.
+ const UnicodeText regex_pattern = UTF8ToUnicodeText(
+ "(?<group1>[0-9])(?<group2>[0-9]+)😋", /*do_copy=*/false);
+ std::unique_ptr<UniLib::RegexPattern> pattern =
+ unilib.CreateRegexPattern(regex_pattern);
+ int status;
+ std::unique_ptr<UniLib::RegexMatcher> matcher;
+
+ matcher = pattern->Matcher(
+ UTF8ToUnicodeText("hello😋😋 0123😋 world", /*do_copy=*/false));
+ EXPECT_TRUE(matcher->Find(&status));
+ EXPECT_EQ(status, UniLib::RegexMatcher::kNoError);
+ EXPECT_EQ(matcher->Start(0, &status), 8);
+ EXPECT_EQ(status, UniLib::RegexMatcher::kNoError);
+ EXPECT_EQ(matcher->Start(1, &status), 8);
+ EXPECT_EQ(status, UniLib::RegexMatcher::kNoError);
+ EXPECT_EQ(matcher->Start(2, &status), 9);
+ EXPECT_EQ(status, UniLib::RegexMatcher::kNoError);
+ EXPECT_EQ(matcher->End(0, &status), 13);
+ EXPECT_EQ(status, UniLib::RegexMatcher::kNoError);
+ EXPECT_EQ(matcher->End(1, &status), 9);
+ EXPECT_EQ(status, UniLib::RegexMatcher::kNoError);
+ EXPECT_EQ(matcher->End(2, &status), 12);
+ EXPECT_EQ(status, UniLib::RegexMatcher::kNoError);
+ EXPECT_EQ(matcher->Group(0, &status).ToUTF8String(), "0123😋");
+ EXPECT_EQ(status, UniLib::RegexMatcher::kNoError);
+ EXPECT_EQ(matcher->Group(1, &status).ToUTF8String(), "0");
+ EXPECT_EQ(status, UniLib::RegexMatcher::kNoError);
+ EXPECT_EQ(matcher->Group(2, &status).ToUTF8String(), "123");
+ EXPECT_EQ(status, UniLib::RegexMatcher::kNoError);
+}
+#endif // LIBTEXTCLASSIFIER_UNILIB_ICU
+
+#ifdef LIBTEXTCLASSIFIER_UNILIB_ICU
+
+TEST(UniLibTest, BreakIterator) {
+ CREATE_UNILIB_FOR_TESTING;
+ const UnicodeText text = UTF8ToUnicodeText("some text", /*do_copy=*/false);
+ std::unique_ptr<UniLib::BreakIterator> iterator =
+ unilib.CreateBreakIterator(text);
+ std::vector<int> break_indices;
+ int break_index = 0;
+ while ((break_index = iterator->Next()) != UniLib::BreakIterator::kDone) {
+ break_indices.push_back(break_index);
+ }
+ EXPECT_THAT(break_indices, ElementsAre(4, 5, 9));
+}
+#endif // LIBTEXTCLASSIFIER_UNILIB_ICU
+
+#ifdef LIBTEXTCLASSIFIER_UNILIB_ICU
+TEST(UniLibTest, BreakIterator4ByteUTF8) {
+ CREATE_UNILIB_FOR_TESTING;
+ const UnicodeText text = UTF8ToUnicodeText("😀😂😋", /*do_copy=*/false);
+ std::unique_ptr<UniLib::BreakIterator> iterator =
+ unilib.CreateBreakIterator(text);
+ std::vector<int> break_indices;
+ int break_index = 0;
+ while ((break_index = iterator->Next()) != UniLib::BreakIterator::kDone) {
+ break_indices.push_back(break_index);
+ }
+ EXPECT_THAT(break_indices, ElementsAre(1, 2, 3));
+}
+#endif // LIBTEXTCLASSIFIER_UNILIB_ICU
+
+#ifndef LIBTEXTCLASSIFIER_UNILIB_JAVAICU
+TEST(UniLibTest, IntegerParse) {
+ CREATE_UNILIB_FOR_TESTING;
+ int result;
+ EXPECT_TRUE(
+ unilib.ParseInt32(UTF8ToUnicodeText("123", /*do_copy=*/false), &result));
+ EXPECT_EQ(result, 123);
+}
+#endif // ndef LIBTEXTCLASSIFIER_UNILIB_JAVAICU
+
+#ifdef LIBTEXTCLASSIFIER_UNILIB_ICU
+TEST(UniLibTest, IntegerParseFullWidth) {
+ CREATE_UNILIB_FOR_TESTING;
+ int result;
+ // The input string here is full width
+ EXPECT_TRUE(unilib.ParseInt32(UTF8ToUnicodeText("１２３", /*do_copy=*/false),
+ &result));
+ EXPECT_EQ(result, 123);
+}
+#endif // LIBTEXTCLASSIFIER_UNILIB_ICU
+
+#ifdef LIBTEXTCLASSIFIER_UNILIB_ICU
+TEST(UniLibTest, IntegerParseFullWidthWithAlpha) {
+ CREATE_UNILIB_FOR_TESTING;
+ int result;
+ // The input string here is full width
+ EXPECT_FALSE(unilib.ParseInt32(UTF8ToUnicodeText("１a３", /*do_copy=*/false),
+ &result));
+}
+#endif // LIBTEXTCLASSIFIER_UNILIB_ICU
+
+} // namespace
+} // namespace libtextclassifier2
diff --git a/zlib-utils.cc b/zlib-utils.cc
new file mode 100644
index 0000000..7e6646f
--- /dev/null
+++ b/zlib-utils.cc
@@ -0,0 +1,269 @@
+/*
+ * Copyright (C) 2017 The Android Open Source Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "zlib-utils.h"
+
+#include <memory>
+
+#include "util/base/logging.h"
+#include "util/flatbuffers.h"
+
+namespace libtextclassifier2 {
+
+std::unique_ptr<ZlibDecompressor> ZlibDecompressor::Instance() {
+ std::unique_ptr<ZlibDecompressor> result(new ZlibDecompressor());
+ if (!result->initialized_) {
+ result.reset();
+ }
+ return result;
+}
+
+ZlibDecompressor::ZlibDecompressor() {
+ memset(&stream_, 0, sizeof(stream_));
+ stream_.zalloc = Z_NULL;
+ stream_.zfree = Z_NULL;
+ initialized_ = (inflateInit(&stream_) == Z_OK);
+}
+
+ZlibDecompressor::~ZlibDecompressor() {
+ if (initialized_) {
+ inflateEnd(&stream_);
+ }
+}
+
+bool ZlibDecompressor::Decompress(const CompressedBuffer* compressed_buffer,
+ std::string* out) {
+ out->resize(compressed_buffer->uncompressed_size());
+ stream_.next_in =
+ reinterpret_cast<const Bytef*>(compressed_buffer->buffer()->Data());
+ stream_.avail_in = compressed_buffer->buffer()->Length();
+ stream_.next_out = reinterpret_cast<Bytef*>(const_cast<char*>(out->c_str()));
+ stream_.avail_out = compressed_buffer->uncompressed_size();
+ return (inflate(&stream_, Z_SYNC_FLUSH) == Z_OK);
+}
+
+std::unique_ptr<ZlibCompressor> ZlibCompressor::Instance() {
+ std::unique_ptr<ZlibCompressor> result(new ZlibCompressor());
+ if (!result->initialized_) {
+ result.reset();
+ }
+ return result;
+}
+
+ZlibCompressor::ZlibCompressor(int level, int tmp_buffer_size) {
+ memset(&stream_, 0, sizeof(stream_));
+ stream_.zalloc = Z_NULL;
+ stream_.zfree = Z_NULL;
+ buffer_size_ = tmp_buffer_size;
+ buffer_.reset(new Bytef[buffer_size_]);
+ initialized_ = (deflateInit(&stream_, level) == Z_OK);
+}
+
+ZlibCompressor::~ZlibCompressor() { deflateEnd(&stream_); }
+
+void ZlibCompressor::Compress(const std::string& uncompressed_content,
+ CompressedBufferT* out) {
+ out->uncompressed_size = uncompressed_content.size();
+ out->buffer.clear();
+ stream_.next_in =
+ reinterpret_cast<const Bytef*>(uncompressed_content.c_str());
+ stream_.avail_in = uncompressed_content.size();
+ stream_.next_out = buffer_.get();
+ stream_.avail_out = buffer_size_;
+ unsigned char* buffer_deflate_start_position =
+ reinterpret_cast<unsigned char*>(buffer_.get());
+ int status;
+ do {
+ // Deflate chunk-wise.
+ // Z_SYNC_FLUSH causes all pending output to be flushed, but doesn't
+ // reset the compression state.
+ // As we do not know how big the compressed buffer will be, we compress
+ // chunk wise and append the flushed content to the output string buffer.
+ // As we store the uncompressed size, we do not have to do this during
+ // decompression.
+ status = deflate(&stream_, Z_SYNC_FLUSH);
+ unsigned char* buffer_deflate_end_position =
+ reinterpret_cast<unsigned char*>(stream_.next_out);
+ if (buffer_deflate_end_position != buffer_deflate_start_position) {
+ out->buffer.insert(out->buffer.end(), buffer_deflate_start_position,
+ buffer_deflate_end_position);
+ stream_.next_out = buffer_deflate_start_position;
+ stream_.avail_out = buffer_size_;
+ } else {
+ break;
+ }
+ } while (status == Z_OK);
+}
+
+// Compress rule fields in the model.
+bool CompressModel(ModelT* model) {
+ std::unique_ptr<ZlibCompressor> zlib_compressor = ZlibCompressor::Instance();
+ if (!zlib_compressor) {
+ TC_LOG(ERROR) << "Cannot compress model.";
+ return false;
+ }
+
+ // Compress regex rules.
+ if (model->regex_model != nullptr) {
+ for (int i = 0; i < model->regex_model->patterns.size(); i++) {
+ RegexModel_::PatternT* pattern = model->regex_model->patterns[i].get();
+ pattern->compressed_pattern.reset(new CompressedBufferT);
+ zlib_compressor->Compress(pattern->pattern,
+ pattern->compressed_pattern.get());
+ pattern->pattern.clear();
+ }
+ }
+
+ // Compress date-time rules.
+ if (model->datetime_model != nullptr) {
+ for (int i = 0; i < model->datetime_model->patterns.size(); i++) {
+ DatetimeModelPatternT* pattern = model->datetime_model->patterns[i].get();
+ for (int j = 0; j < pattern->regexes.size(); j++) {
+ DatetimeModelPattern_::RegexT* regex = pattern->regexes[j].get();
+ regex->compressed_pattern.reset(new CompressedBufferT);
+ zlib_compressor->Compress(regex->pattern,
+ regex->compressed_pattern.get());
+ regex->pattern.clear();
+ }
+ }
+ for (int i = 0; i < model->datetime_model->extractors.size(); i++) {
+ DatetimeModelExtractorT* extractor =
+ model->datetime_model->extractors[i].get();
+ extractor->compressed_pattern.reset(new CompressedBufferT);
+ zlib_compressor->Compress(extractor->pattern,
+ extractor->compressed_pattern.get());
+ extractor->pattern.clear();
+ }
+ }
+ return true;
+}
+
+namespace {
+
+bool DecompressBuffer(const CompressedBufferT* compressed_pattern,
+ ZlibDecompressor* zlib_decompressor,
+ std::string* uncompressed_pattern) {
+ std::string packed_pattern =
+ PackFlatbuffer<CompressedBuffer>(compressed_pattern);
+ if (!zlib_decompressor->Decompress(
+ LoadAndVerifyFlatbuffer<CompressedBuffer>(packed_pattern),
+ uncompressed_pattern)) {
+ return false;
+ }
+ return true;
+}
+
+} // namespace
+
+bool DecompressModel(ModelT* model) {
+ std::unique_ptr<ZlibDecompressor> zlib_decompressor =
+ ZlibDecompressor::Instance();
+ if (!zlib_decompressor) {
+ TC_LOG(ERROR) << "Cannot initialize decompressor.";
+ return false;
+ }
+
+ // Decompress regex rules.
+ if (model->regex_model != nullptr) {
+ for (int i = 0; i < model->regex_model->patterns.size(); i++) {
+ RegexModel_::PatternT* pattern = model->regex_model->patterns[i].get();
+ if (!DecompressBuffer(pattern->compressed_pattern.get(),
+ zlib_decompressor.get(), &pattern->pattern)) {
+ TC_LOG(ERROR) << "Cannot decompress pattern: " << i;
+ return false;
+ }
+ pattern->compressed_pattern.reset(nullptr);
+ }
+ }
+
+ // Decompress date-time rules.
+ if (model->datetime_model != nullptr) {
+ for (int i = 0; i < model->datetime_model->patterns.size(); i++) {
+ DatetimeModelPatternT* pattern = model->datetime_model->patterns[i].get();
+ for (int j = 0; j < pattern->regexes.size(); j++) {
+ DatetimeModelPattern_::RegexT* regex = pattern->regexes[j].get();
+ if (!DecompressBuffer(regex->compressed_pattern.get(),
+ zlib_decompressor.get(), ®ex->pattern)) {
+ TC_LOG(ERROR) << "Cannot decompress pattern: " << i << " " << j;
+ return false;
+ }
+ regex->compressed_pattern.reset(nullptr);
+ }
+ }
+ for (int i = 0; i < model->datetime_model->extractors.size(); i++) {
+ DatetimeModelExtractorT* extractor =
+ model->datetime_model->extractors[i].get();
+ if (!DecompressBuffer(extractor->compressed_pattern.get(),
+ zlib_decompressor.get(), &extractor->pattern)) {
+ TC_LOG(ERROR) << "Cannot decompress pattern: " << i;
+ return false;
+ }
+ extractor->compressed_pattern.reset(nullptr);
+ }
+ }
+ return true;
+}
+
+std::string CompressSerializedModel(const std::string& model) {
+ std::unique_ptr<ModelT> unpacked_model = UnPackModel(model.c_str());
+ TC_CHECK(unpacked_model != nullptr);
+ TC_CHECK(CompressModel(unpacked_model.get()));
+ flatbuffers::FlatBufferBuilder builder;
+ FinishModelBuffer(builder, Model::Pack(builder, unpacked_model.get()));
+ return std::string(reinterpret_cast<const char*>(builder.GetBufferPointer()),
+ builder.GetSize());
+}
+
+std::unique_ptr<UniLib::RegexPattern> UncompressMakeRegexPattern(
+ const UniLib& unilib, const flatbuffers::String* uncompressed_pattern,
+ const CompressedBuffer* compressed_pattern, ZlibDecompressor* decompressor,
+ std::string* result_pattern_text) {
+ UnicodeText unicode_regex_pattern;
+ std::string decompressed_pattern;
+ if (compressed_pattern != nullptr &&
+ compressed_pattern->buffer() != nullptr) {
+ if (decompressor == nullptr ||
+ !decompressor->Decompress(compressed_pattern, &decompressed_pattern)) {
+ TC_LOG(ERROR) << "Cannot decompress pattern.";
+ return nullptr;
+ }
+ unicode_regex_pattern =
+ UTF8ToUnicodeText(decompressed_pattern.data(),
+ decompressed_pattern.size(), /*do_copy=*/false);
+ } else {
+ if (uncompressed_pattern == nullptr) {
+ TC_LOG(ERROR) << "Cannot load uncompressed pattern.";
+ return nullptr;
+ }
+ unicode_regex_pattern =
+ UTF8ToUnicodeText(uncompressed_pattern->c_str(),
+ uncompressed_pattern->Length(), /*do_copy=*/false);
+ }
+
+ if (result_pattern_text != nullptr) {
+ *result_pattern_text = unicode_regex_pattern.ToUTF8String();
+ }
+
+ std::unique_ptr<UniLib::RegexPattern> regex_pattern =
+ unilib.CreateRegexPattern(unicode_regex_pattern);
+ if (!regex_pattern) {
+ TC_LOG(ERROR) << "Could not create pattern: "
+ << unicode_regex_pattern.ToUTF8String();
+ }
+ return regex_pattern;
+}
+
+} // namespace libtextclassifier2
diff --git a/zlib-utils.h b/zlib-utils.h
new file mode 100644
index 0000000..136f4d2
--- /dev/null
+++ b/zlib-utils.h
@@ -0,0 +1,79 @@
+/*
+ * Copyright (C) 2017 The Android Open Source Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+// Functions to compress and decompress low entropy entries in the model.
+
+#ifndef LIBTEXTCLASSIFIER_ZLIB_UTILS_H_
+#define LIBTEXTCLASSIFIER_ZLIB_UTILS_H_
+
+#include <memory>
+
+#include "model_generated.h"
+#include "util/utf8/unilib.h"
+#include "zlib.h"
+
+namespace libtextclassifier2 {
+
+class ZlibDecompressor {
+ public:
+ static std::unique_ptr<ZlibDecompressor> Instance();
+ ~ZlibDecompressor();
+
+ bool Decompress(const CompressedBuffer* compressed_buffer, std::string* out);
+
+ private:
+ ZlibDecompressor();
+ z_stream stream_;
+ bool initialized_;
+};
+
+class ZlibCompressor {
+ public:
+ static std::unique_ptr<ZlibCompressor> Instance();
+ ~ZlibCompressor();
+
+ void Compress(const std::string& uncompressed_content,
+ CompressedBufferT* out);
+
+ private:
+ explicit ZlibCompressor(int level = Z_BEST_COMPRESSION,
+ // Tmp. buffer size was set based on the current set
+ // of patterns to be compressed.
+ int tmp_buffer_size = 64 * 1024);
+ z_stream stream_;
+ std::unique_ptr<Bytef[]> buffer_;
+ unsigned int buffer_size_;
+ bool initialized_;
+};
+
+// Compresses regex and datetime rules in the model in place.
+bool CompressModel(ModelT* model);
+
+// Decompresses regex and datetime rules in the model in place.
+bool DecompressModel(ModelT* model);
+
+// Compresses regex and datetime rules in the model.
+std::string CompressSerializedModel(const std::string& model);
+
+// Create and compile a regex pattern from optionally compressed pattern.
+std::unique_ptr<UniLib::RegexPattern> UncompressMakeRegexPattern(
+ const UniLib& unilib, const flatbuffers::String* uncompressed_pattern,
+ const CompressedBuffer* compressed_pattern, ZlibDecompressor* decompressor,
+ std::string* result_pattern_text = nullptr);
+
+} // namespace libtextclassifier2
+
+#endif // LIBTEXTCLASSIFIER_ZLIB_UTILS_H_
diff --git a/zlib-utils_test.cc b/zlib-utils_test.cc
new file mode 100644
index 0000000..155f14f
--- /dev/null
+++ b/zlib-utils_test.cc
@@ -0,0 +1,98 @@
+/*
+ * Copyright (C) 2017 The Android Open Source Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "zlib-utils.h"
+
+#include <memory>
+
+#include "model_generated.h"
+#include "gmock/gmock.h"
+#include "gtest/gtest.h"
+
+namespace libtextclassifier2 {
+
+TEST(ZlibUtilsTest, CompressModel) {
+ ModelT model;
+ model.regex_model.reset(new RegexModelT);
+ model.regex_model->patterns.emplace_back(new RegexModel_::PatternT);
+ model.regex_model->patterns.back()->pattern = "this is a test pattern";
+ model.regex_model->patterns.emplace_back(new RegexModel_::PatternT);
+ model.regex_model->patterns.back()->pattern = "this is a second test pattern";
+
+ model.datetime_model.reset(new DatetimeModelT);
+ model.datetime_model->patterns.emplace_back(new DatetimeModelPatternT);
+ model.datetime_model->patterns.back()->regexes.emplace_back(
+ new DatetimeModelPattern_::RegexT);
+ model.datetime_model->patterns.back()->regexes.back()->pattern =
+ "an example datetime pattern";
+ model.datetime_model->extractors.emplace_back(new DatetimeModelExtractorT);
+ model.datetime_model->extractors.back()->pattern =
+ "an example datetime extractor";
+
+ // Compress the model.
+ EXPECT_TRUE(CompressModel(&model));
+
+ // Sanity check that uncompressed field is removed.
+ EXPECT_TRUE(model.regex_model->patterns[0]->pattern.empty());
+ EXPECT_TRUE(model.regex_model->patterns[1]->pattern.empty());
+ EXPECT_TRUE(model.datetime_model->patterns[0]->regexes[0]->pattern.empty());
+ EXPECT_TRUE(model.datetime_model->extractors[0]->pattern.empty());
+
+ // Pack and load the model.
+ flatbuffers::FlatBufferBuilder builder;
+ builder.Finish(Model::Pack(builder, &model));
+ const Model* compressed_model =
+ GetModel(reinterpret_cast<const char*>(builder.GetBufferPointer()));
+ ASSERT_TRUE(compressed_model != nullptr);
+
+ // Decompress the fields again and check that they match the original.
+ std::unique_ptr<ZlibDecompressor> decompressor = ZlibDecompressor::Instance();
+ ASSERT_TRUE(decompressor != nullptr);
+ std::string uncompressed_pattern;
+ EXPECT_TRUE(decompressor->Decompress(
+ compressed_model->regex_model()->patterns()->Get(0)->compressed_pattern(),
+ &uncompressed_pattern));
+ EXPECT_EQ(uncompressed_pattern, "this is a test pattern");
+ EXPECT_TRUE(decompressor->Decompress(
+ compressed_model->regex_model()->patterns()->Get(1)->compressed_pattern(),
+ &uncompressed_pattern));
+ EXPECT_EQ(uncompressed_pattern, "this is a second test pattern");
+ EXPECT_TRUE(decompressor->Decompress(compressed_model->datetime_model()
+ ->patterns()
+ ->Get(0)
+ ->regexes()
+ ->Get(0)
+ ->compressed_pattern(),
+ &uncompressed_pattern));
+ EXPECT_EQ(uncompressed_pattern, "an example datetime pattern");
+ EXPECT_TRUE(decompressor->Decompress(compressed_model->datetime_model()
+ ->extractors()
+ ->Get(0)
+ ->compressed_pattern(),
+ &uncompressed_pattern));
+ EXPECT_EQ(uncompressed_pattern, "an example datetime extractor");
+
+ EXPECT_TRUE(DecompressModel(&model));
+ EXPECT_EQ(model.regex_model->patterns[0]->pattern, "this is a test pattern");
+ EXPECT_EQ(model.regex_model->patterns[1]->pattern,
+ "this is a second test pattern");
+ EXPECT_EQ(model.datetime_model->patterns[0]->regexes[0]->pattern,
+ "an example datetime pattern");
+ EXPECT_EQ(model.datetime_model->extractors[0]->pattern,
+ "an example datetime extractor");
+}
+
+} // namespace libtextclassifier2
