/*
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* Copyright (C) 2018 The Android Open Source Project
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*
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* Licensed under the Apache License, Version 2.0 (the "License");
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* you may not use this file except in compliance with the License.
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* You may obtain a copy of the License at
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*
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* http://www.apache.org/licenses/LICENSE-2.0
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*
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* Unless required by applicable law or agreed to in writing, software
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* distributed under the License is distributed on an "AS IS" BASIS,
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* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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* See the License for the specific language governing permissions and
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* limitations under the License.
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*/
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#include "actions/lua-actions.h"
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#include "utils/base/logging.h"
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#include "utils/lua-utils.h"
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#ifdef __cplusplus
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extern "C" {
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#endif
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#include "lauxlib.h"
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#include "lualib.h"
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#ifdef __cplusplus
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}
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#endif
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namespace libtextclassifier3 {
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namespace {
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TensorView<float> GetTensorViewForOutput(
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const TfLiteModelExecutor* model_executor,
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const tflite::Interpreter* interpreter, int output) {
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if (output < 0 || model_executor == nullptr || interpreter == nullptr) {
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return TensorView<float>::Invalid();
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}
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return model_executor->OutputView<float>(output, interpreter);
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}
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} // namespace
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int LuaActionsSuggestions::TensorViewIterator::Item(
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const TensorView<float>* tensor, const int64 index,
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lua_State* state) const {
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lua_pushnumber(state, tensor->data()[index]);
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return 1;
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}
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std::unique_ptr<LuaActionsSuggestions>
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LuaActionsSuggestions::CreateLuaActionsSuggestions(
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const std::string& snippet, const Conversation& conversation,
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const TfLiteModelExecutor* model_executor,
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const TensorflowLiteModelSpec* model_spec,
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const tflite::Interpreter* interpreter,
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const reflection::Schema* actions_entity_data_schema,
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const reflection::Schema* annotations_entity_data_schema) {
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auto lua_actions =
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std::unique_ptr<LuaActionsSuggestions>(new LuaActionsSuggestions(
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snippet, conversation, model_executor, model_spec, interpreter,
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actions_entity_data_schema, annotations_entity_data_schema));
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if (!lua_actions->Initialize()) {
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TC3_LOG(ERROR)
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<< "Could not initialize lua environment for actions suggestions.";
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return nullptr;
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}
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return lua_actions;
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}
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LuaActionsSuggestions::LuaActionsSuggestions(
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const std::string& snippet, const Conversation& conversation,
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const TfLiteModelExecutor* model_executor,
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const TensorflowLiteModelSpec* model_spec,
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const tflite::Interpreter* interpreter,
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const reflection::Schema* actions_entity_data_schema,
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const reflection::Schema* annotations_entity_data_schema)
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: snippet_(snippet),
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conversation_(conversation),
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conversation_iterator_(annotations_entity_data_schema, this),
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actions_scores_(
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model_spec == nullptr
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? TensorView<float>::Invalid()
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: GetTensorViewForOutput(model_executor, interpreter,
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model_spec->output_actions_scores())),
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smart_reply_scores_(
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model_spec == nullptr
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? TensorView<float>::Invalid()
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: GetTensorViewForOutput(model_executor, interpreter,
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model_spec->output_replies_scores())),
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sensitivity_score_(model_spec == nullptr
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? TensorView<float>::Invalid()
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: GetTensorViewForOutput(
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model_executor, interpreter,
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model_spec->output_sensitive_topic_score())),
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triggering_score_(
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model_spec == nullptr
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? TensorView<float>::Invalid()
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: GetTensorViewForOutput(model_executor, interpreter,
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model_spec->output_triggering_score())),
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actions_entity_data_schema_(actions_entity_data_schema),
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annotations_entity_data_schema_(annotations_entity_data_schema) {}
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bool LuaActionsSuggestions::Initialize() {
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return RunProtected([this] {
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LoadDefaultLibraries();
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// Expose conversation message stream.
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conversation_iterator_.NewIterator("messages",
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&conversation_.messages, state_);
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lua_setglobal(state_, "messages");
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// Expose ML model output.
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lua_newtable(state_);
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{
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tensor_iterator_.NewIterator("actions_scores", &actions_scores_,
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state_);
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lua_setfield(state_, /*idx=*/-2, "actions_scores");
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}
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{
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tensor_iterator_.NewIterator("reply_scores", &smart_reply_scores_,
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state_);
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lua_setfield(state_, /*idx=*/-2, "reply_scores");
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}
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{
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tensor_iterator_.NewIterator("sensitivity", &sensitivity_score_,
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state_);
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lua_setfield(state_, /*idx=*/-2, "sensitivity");
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}
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{
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tensor_iterator_.NewIterator("triggering_score",
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&triggering_score_, state_);
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lua_setfield(state_, /*idx=*/-2, "triggering_score");
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}
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lua_setglobal(state_, "model");
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return LUA_OK;
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}) == LUA_OK;
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}
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bool LuaActionsSuggestions::SuggestActions(
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std::vector<ActionSuggestion>* actions) {
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if (luaL_loadbuffer(state_, snippet_.data(), snippet_.size(),
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/*name=*/nullptr) != LUA_OK) {
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TC3_LOG(ERROR) << "Could not load actions suggestions snippet.";
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return false;
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}
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if (lua_pcall(state_, /*nargs=*/0, /*nargs=*/1, /*errfunc=*/0) != LUA_OK) {
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TC3_LOG(ERROR) << "Could not run actions suggestions snippet.";
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return false;
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}
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if (RunProtected(
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[this, actions] {
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return ReadActions(actions_entity_data_schema_,
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annotations_entity_data_schema_, this, actions);
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},
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/*num_args=*/1) != LUA_OK) {
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TC3_LOG(ERROR) << "Could not read lua result.";
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return false;
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}
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return true;
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}
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} // namespace libtextclassifier3
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