// Copyright 2019 DeepMind Technologies Ltd. All rights reserved.
//
// 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 "open_spiel/algorithms/alpha_zero/alpha_zero.h"

#include <cstdlib>
#include <iostream>
#include <memory>
#include <random>
#include <string>
#include <vector>

#include "open_spiel/abseil-cpp/absl/algorithm/container.h"
#include "open_spiel/abseil-cpp/absl/random/uniform_real_distribution.h"
#include "open_spiel/abseil-cpp/absl/strings/str_cat.h"
#include "open_spiel/abseil-cpp/absl/strings/str_join.h"
#include "open_spiel/abseil-cpp/absl/strings/str_split.h"
#include "open_spiel/abseil-cpp/absl/synchronization/mutex.h"
#include "open_spiel/abseil-cpp/absl/time/clock.h"
#include "open_spiel/abseil-cpp/absl/time/time.h"
#include "open_spiel/algorithms/alpha_zero/device_manager.h"
#include "open_spiel/algorithms/alpha_zero/vpevaluator.h"
#include "open_spiel/algorithms/alpha_zero/vpnet.h"
#include "open_spiel/algorithms/mcts.h"
#include "open_spiel/spiel.h"
#include "open_spiel/spiel_utils.h"
#include "open_spiel/utils/circular_buffer.h"
#include "open_spiel/utils/data_logger.h"
#include "open_spiel/utils/file.h"
#include "open_spiel/utils/json.h"
#include "open_spiel/utils/logger.h"
#include "open_spiel/utils/lru_cache.h"
#include "open_spiel/utils/stats.h"
#include "open_spiel/utils/thread.h"
#include "open_spiel/utils/threaded_queue.h"

namespace open_spiel::algorithms {

struct Trajectory {
  struct State {
    std::vector<float> observation;
    open_spiel::Player current_player;
    std::vector<open_spiel::Action> legal_actions;
    open_spiel::Action action;
    open_spiel::ActionsAndProbs policy;
    double value;
  };

  std::vector<State> states;
  std::vector<double> returns;
};

Trajectory PlayGame(
    Logger* logger,
    int game_num,
    const open_spiel::Game& game,
    std::vector<std::unique_ptr<MCTSBot>>* bots,
    std::mt19937* rng, double temperature, int temperature_drop,
    double cutoff_value, bool verbose = false) {
  std::unique_ptr<open_spiel::State> state = game.NewInitialState();
  std::vector<std::string> history;
  Trajectory trajectory;

  while (true) {
    open_spiel::Player player = state->CurrentPlayer();
    std::unique_ptr<SearchNode> root = (*bots)[player]->MCTSearch(*state);
    open_spiel::ActionsAndProbs policy;
    policy.reserve(root->children.size());
    for (const SearchNode& c : root->children) {
      policy.emplace_back(
          c.action, std::pow(c.explore_count, 1.0 / temperature));
    }
    NormalizePolicy(&policy);
    open_spiel::Action action;
    if (history.size() >= temperature_drop) {
      action = root->BestChild().action;
    } else {
      action = open_spiel::SampleAction(policy, *rng).first;
    }

    double root_value = root->total_reward / root->explore_count;
    trajectory.states.push_back(Trajectory::State{
        state->ObservationTensor(), player,
        state->LegalActions(), action, std::move(policy), root_value});
    std::string action_str = state->ActionToString(player, action);
    history.push_back(action_str);
    state->ApplyAction(action);
    if (verbose) {
      logger->Print("Player: %d, action: %s", player, action_str);
    }
    if (state->IsTerminal()) {
      trajectory.returns = state->Returns();
      break;
    } else if (std::abs(root_value) > cutoff_value) {
      trajectory.returns.resize(2);
      trajectory.returns[player] = root_value;
      trajectory.returns[1 - player] = -root_value;
      break;
    }
  }

  logger->Print(
      "Game %d: Returns: %s; Actions: %s", game_num,
      absl::StrJoin(trajectory.returns, " "),
      absl::StrJoin(history, " "));
  return trajectory;
}

std::unique_ptr<MCTSBot> InitAZBot(
    const AlphaZeroConfig& config, const open_spiel::Game& game,
    std::shared_ptr<Evaluator> evaluator, bool evaluation) {
  return std::make_unique<MCTSBot>(
      game,
      std::move(evaluator),
      config.uct_c,
      config.max_simulations,
      /*max_memory_mb=*/ 10,
      /*solve=*/ false,
      /*seed=*/ 0,
      /*verbose=*/ false,
      ChildSelectionPolicy::PUCT,
      evaluation ? 0 : config.policy_alpha,
      evaluation ? 0 : config.policy_epsilon);
}

// An actor thread runner that generates games and returns trajectories.
void actor(const open_spiel::Game& game, const AlphaZeroConfig& config, int num,
           ThreadedQueue<Trajectory>* trajectory_queue,
           std::shared_ptr<VPNetEvaluator> vp_eval,
           StopToken* stop) {
  std::unique_ptr<Logger> logger;
  if (num < 20) {  // Limit the number of open files.
    logger.reset(new FileLogger(config.path, absl::StrCat("actor-", num)));
  } else {
    logger.reset(new NoopLogger());
  }
  std::mt19937 rng;
  absl::uniform_real_distribution<double> dist(0.0, 1.0);
  std::vector<std::unique_ptr<MCTSBot>> bots;
  bots.reserve(2);
  for (int player = 0; player < 2; player++) {
    bots.push_back(InitAZBot(config, game, vp_eval, false));
  }
  for (int game_num = 1; !stop->StopRequested(); ++game_num) {
    double cutoff = (dist(rng) < config.cutoff_probability
                     ? config.cutoff_value : game.MaxUtility() + 1);
    if (!trajectory_queue->Push(
            PlayGame(logger.get(), game_num, game, &bots, &rng,
                     config.temperature, config.temperature_drop, cutoff),
            absl::Seconds(10))) {
      logger->Print("Failed to push a trajectory after 10 seconds.");
    }
  }
  logger->Print("Got a quit.");
}

class EvalResults {
 public:
  explicit EvalResults(int count, int evaluation_window) {
    results_.reserve(count);
    for (int i = 0; i < count; ++i) {
      results_.emplace_back(evaluation_window);
    }
  }

  // How many evals per difficulty.
  int EvalCount() {
    absl::MutexLock lock(&m_);
    return eval_num_ / results_.size();
  }

  // Which eval to do next: difficulty, player0.
  std::pair<int, bool> Next() {
    absl::MutexLock lock(&m_);
    int next = eval_num_ % (results_.size() * 2);
    eval_num_ += 1;
    return {next / 2, next % 2};
  }

  void Add(int i, double value) {
    absl::MutexLock lock(&m_);
    results_[i].Add(value);
  }

  std::vector<double> AvgResults() {
    absl::MutexLock lock(&m_);
    std::vector<double> out;
    out.reserve(results_.size());
    for (const auto& result : results_) {
      out.push_back(result.Empty() ? 0
                                   : (absl::c_accumulate(result.Data(), 0.0) /
                                      result.Size()));
    }
    return out;
  }

 private:
  std::vector<CircularBuffer<double>> results_;
  int eval_num_ = 0;
  absl::Mutex m_;
};

// A thread that plays vs standard MCTS.
void evaluator(const open_spiel::Game& game, const AlphaZeroConfig& config,
               int num, EvalResults* results,
               std::shared_ptr<VPNetEvaluator> vp_eval, StopToken* stop) {
  FileLogger logger(config.path, absl::StrCat("evaluator-", num));
  std::mt19937 rng;
  auto rand_evaluator = std::make_shared<RandomRolloutEvaluator>(1, num);

  for (int game_num = 1; !stop->StopRequested(); ++game_num) {
    auto [difficulty, first] = results->Next();
    int az_player = first ? 0 : 1;
    int rand_max_simulations = config.max_simulations * std::pow(
        10, difficulty / 2.0);
    std::vector<std::unique_ptr<MCTSBot>> bots;
    bots.reserve(2);
    bots.push_back(InitAZBot(config, game, vp_eval, true));
    bots.push_back(std::make_unique<MCTSBot>(
            game,
            rand_evaluator,
            config.uct_c,
            rand_max_simulations,
            /*max_memory_mb=*/1000,
            /*solve=*/true,
            /*seed=*/num * 1000 + game_num,
            /*verbose=*/false,
            ChildSelectionPolicy::UCT));
    if (az_player == 1) {
      std::swap(bots[0], bots[1]);
    }

    logger.Print("Running MCTS with %d simulations", rand_max_simulations);
    Trajectory trajectory = PlayGame(
        &logger, game_num, game, &bots, &rng, /*temperature=*/ 1,
        /*temperature_drop=*/ 0, /*cutoff_value=*/ game.MaxUtility() + 1);

    results->Add(difficulty, trajectory.returns[az_player]);
    logger.Print("Game %d: AZ: %5.2f, MCTS: %5.2f, MCTS-sims: %d, length: %d",
                 game_num, trajectory.returns[az_player],
                 trajectory.returns[1 - az_player], rand_max_simulations,
                 trajectory.states.size());
  }
  logger.Print("Got a quit.");
}

void learner(const open_spiel::Game& game,
             const AlphaZeroConfig& config,
             DeviceManager* device_manager,
             std::shared_ptr<VPNetEvaluator> eval,
             ThreadedQueue<Trajectory>* trajectory_queue,
             EvalResults* eval_results,
             StopToken* stop) {
  FileLogger logger(config.path, "learner");
  DataLoggerJsonLines data_logger(config.path, "learner", true);
  std::mt19937 rng;

  int device_id = 0;
  logger.Print("Running the learner on device %d: %s", device_id,
               device_manager->Get(0, device_id)->Device());

  CircularBuffer<VPNetModel::TrainInputs> replay_buffer(
      config.replay_buffer_size);
  int learn_rate = config.replay_buffer_size / config.replay_buffer_reuse;
  int64_t total_trajectories = 0;

  const int stage_count = 7;
  std::vector<open_spiel::BasicStats> value_accuracies(stage_count);
  std::vector<open_spiel::BasicStats> value_predictions(stage_count);
  open_spiel::BasicStats game_lengths;
  open_spiel::HistogramNumbered game_lengths_hist(game.MaxGameLength() + 1);

  open_spiel::HistogramNamed outcomes({"Player1", "Player2", "Draw"});
  // Actor threads have likely been contributing for a while, so put `last` in
  // the past to avoid a giant spike on the first step.
  absl::Time last = absl::Now() - absl::Seconds(60);
  for (int step = 1; !stop->StopRequested() &&
                     (config.max_steps == 0 || step <= config.max_steps);
       ++step) {
    outcomes.Reset();
    game_lengths.Reset();
    game_lengths_hist.Reset();
    for (auto& value_accuracy : value_accuracies) {
      value_accuracy.Reset();
    }
    for (auto& value_prediction : value_predictions) {
      value_prediction.Reset();
    }

    // Collect trajectories
    int queue_size = trajectory_queue->Size();
    int num_states = 0;
    int num_trajectories = 0;
    while (!stop->StopRequested() && num_states < learn_rate) {
      absl::optional<Trajectory> trajectory = trajectory_queue->Pop();
      if (trajectory) {
        num_trajectories += 1;
        total_trajectories += 1;
        game_lengths.Add(trajectory->states.size());
        game_lengths_hist.Add(trajectory->states.size());

        double p1_outcome = trajectory->returns[0];
        outcomes.Add(p1_outcome > 0 ? 0 : (p1_outcome < 0 ? 1 : 2));

        for (const Trajectory::State& state : trajectory->states) {
          replay_buffer.Add(
              VPNetModel::TrainInputs{
                  state.legal_actions,
                  state.observation,
                  state.policy,
                  p1_outcome});
          num_states += 1;
        }

        for (int stage = 0; stage < stage_count; ++stage) {
          // Scale for the length of the game
          int index = (trajectory->states.size() - 1) *
                      static_cast<double>(stage) / (stage_count - 1);
          const Trajectory::State& s = trajectory->states[index];
          value_accuracies[stage].Add(
              (s.value >= 0) == (trajectory->returns[s.current_player] >= 0));
          value_predictions[stage].Add(abs(s.value));
        }
      }
    }
    absl::Time now = absl::Now();
    double seconds = absl::ToDoubleSeconds(now - last);
    logger.Print("Step: %d", step);
    logger.Print(
        "Collected %5d states from %3d games, %.1f states/s; "
        "%.1f states/(s*actor), game length: %.1f",
         num_states, num_trajectories, num_states / seconds,
         num_states / (config.actors * seconds),
         static_cast<double>(num_states) / num_trajectories);
    logger.Print("Queue size: %d. Buffer size: %d. States seen: %d",
                 queue_size, replay_buffer.Size(), replay_buffer.TotalAdded());

    if (stop->StopRequested()) {
      break;
    }

    last = now;

    VPNetModel::LossInfo losses;
    {  // Extra scope to return the device for use for inference asap.
      DeviceManager::DeviceLoan learn_model =
          device_manager->Get(config.train_batch_size, device_id);

      // Learn from them.
      for (int i = 0; i < replay_buffer.Size() / config.train_batch_size; i++) {
        losses += learn_model->Learn(replay_buffer.Sample(
            &rng, config.train_batch_size));
      }
    }

    // Always save a checkpoint, either for keeping or for loading the weights
    // to the other sessions. It only allows numbers, so use -1 as "latest".
    std::string checkpoint_path =
        device_manager->Get(0, device_id)->SaveCheckpoint(
            step % config.checkpoint_freq == 0 ? step : -1);
    if (device_manager->Count() > 0) {
      for (int i = 0; i < device_manager->Count(); ++i) {
        if (i != device_id) {
          device_manager->Get(0, i)->LoadCheckpoint(checkpoint_path);
        }
      }
    }
    logger.Print("Checkpoint saved: %s", checkpoint_path);

    DataLogger::Record record = {
        {"step", step},
        {"total_states", replay_buffer.TotalAdded()},
        {"states_per_s", num_states / seconds},
        {"states_per_s_actor", num_states / (config.actors * seconds)},
        {"total_trajectories", total_trajectories},
        {"trajectories_per_s", num_trajectories / seconds},
        {"queue_size", queue_size},
        {"game_length", game_lengths.ToJson()},
        {"game_length_hist", game_lengths_hist.ToJson()},
        {"outcomes", outcomes.ToJson()},
        {"value_accuracy", json::TransformToArray(
            value_accuracies, [](auto v){ return v.ToJson(); })},
        {"value_prediction", json::TransformToArray(
            value_predictions, [](auto v){ return v.ToJson(); })},
        {"eval", json::Object({
            {"count", eval_results->EvalCount()},
            {"results", json::CastToArray(eval_results->AvgResults())},
        })},
        {"batch_size", eval->BatchSizeStats().ToJson()},
        {"batch_size_hist", eval->BatchSizeHistogram().ToJson()},
        {"loss", json::Object({
             {"policy", losses.Policy()},
             {"value", losses.Value()},
             {"l2reg", losses.L2()},
             {"sum", losses.Total()},
        })},
    };
    eval->ResetBatchSizeStats();
    logger.Print("Losses: policy: %.4f, value: %.4f, l2: %.4f, sum: %.4f",
                 losses.Policy(), losses.Value(), losses.L2(), losses.Total());

    LRUCacheInfo cache_info = eval->CacheInfo();
    if (cache_info.size > 0) {
      logger.Print(absl::StrFormat(
          "Cache size: %d/%d: %.1f%%, hits: %d, misses: %d, hit rate: %.3f%%",
          cache_info.size, cache_info.max_size, 100.0 * cache_info.Usage(),
          cache_info.hits, cache_info.misses, 100.0 * cache_info.HitRate()));
      eval->ClearCache();
    }
    record.emplace("cache", json::Object({
        {"size", cache_info.size},
        {"max_size", cache_info.max_size},
        {"usage", cache_info.Usage()},
        {"requests", cache_info.Total()},
        {"requests_per_s", cache_info.Total() / seconds},
        {"hits", cache_info.hits},
        {"misses", cache_info.misses},
        {"misses_per_s", cache_info.misses / seconds},
        {"hit_rate", cache_info.HitRate()},
    }));

    data_logger.Write(record);
    logger.Print("");
  }
}

bool AlphaZero(AlphaZeroConfig config, StopToken* stop) {
  std::shared_ptr<const open_spiel::Game> game =
      open_spiel::LoadGame(config.game);

  open_spiel::GameType game_type = game->GetType();
  if (game->NumPlayers() != 2)
    open_spiel::SpielFatalError("AlphaZero can only handle 2-player games.");
  if (game_type.reward_model != open_spiel::GameType::RewardModel::kTerminal)
    open_spiel::SpielFatalError("Game must have terminal rewards.");
  if (game_type.dynamics != open_spiel::GameType::Dynamics::kSequential)
    open_spiel::SpielFatalError("Game must have sequential turns.");
  if (game_type.chance_mode != open_spiel::GameType::ChanceMode::kDeterministic)
    open_spiel::SpielFatalError("Game must be deterministic.");

  file::Mkdirs(config.path);
  if (!file::IsDirectory(config.path)) {
    std::cerr << config.path << " is not a directory." << std::endl;
    return false;
  }

  std::cout << "Logging directory: " << config.path << std::endl;

  if (config.graph_def.empty()) {
    config.graph_def = "vpnet.pb";
    std::string model_path = absl::StrCat(config.path, "/", config.graph_def);
    if (file::Exists(model_path)) {
      std::cout << "Overwriting existing model: " << model_path << std::endl;
    } else {
      std::cout << "Creating model: " << model_path << std::endl;
    }
    SPIEL_CHECK_TRUE(CreateGraphDef(
        *game, config.learning_rate, config.weight_decay,
        config.path, config.graph_def,
        config.nn_model, config.nn_width, config.nn_depth));
  } else {
    std::string model_path = absl::StrCat(config.path, "/", config.graph_def);
    if (file::Exists(model_path)) {
      std::cout << "Using existing model: " << model_path << std::endl;
    } else {
      std::cout << "Model not found: " << model_path << std::endl;
    }
  }

  std::cout << "Playing game: " << config.game << std::endl;

  config.inference_batch_size = std::max(1, std::min(
      config.inference_batch_size, config.actors + config.evaluators));

  config.inference_threads = std::max(1, std::min(
      config.inference_threads, (1 + config.actors + config.evaluators) / 2));

  {
    file::File fd(config.path + "/config.json", "w");
    fd.Write(json::ToString(config.ToJson(), true) + "\n");
  }

  DeviceManager device_manager;
  for (const absl::string_view& device : absl::StrSplit(config.devices, ',')) {
    device_manager.AddDevice(VPNetModel(
        *game, config.path, config.graph_def, std::string(device)));
  }

  if (device_manager.Count() == 0) {
    std::cerr << "No devices specified?" << std::endl;
    return false;
  }

  {  // Make sure they're all in sync.
    std::string first_checkpoint = device_manager.Get(0)->SaveCheckpoint(0);
    for (int i = 1; i < device_manager.Count(); ++i) {
      device_manager.Get(0, i)->LoadCheckpoint(first_checkpoint);
    }
  }

  auto eval = std::make_shared<VPNetEvaluator>(
      &device_manager, config.inference_batch_size, config.inference_threads,
      config.inference_cache, (config.actors + config.evaluators) / 16);

  ThreadedQueue<Trajectory> trajectory_queue(
      config.replay_buffer_size / config.replay_buffer_reuse);

  EvalResults eval_results(config.eval_levels, config.evaluation_window);

  std::vector<Thread> actors;
  actors.reserve(config.actors);
  for (int i = 0; i < config.actors; ++i) {
    actors.emplace_back(
        [&, i]() { actor(*game, config, i, &trajectory_queue, eval, stop); });
  }
  std::vector<Thread> evaluators;
  evaluators.reserve(config.evaluators);
  for (int i = 0; i < config.evaluators; ++i) {
    evaluators.emplace_back(
        [&, i]() { evaluator(*game, config, i, &eval_results, eval, stop); });
  }
  learner(*game, config, &device_manager, eval, &trajectory_queue,
          &eval_results, stop);

  if (!stop->StopRequested()) {
    stop->Stop();
  }

  // Empty the queue so that the actors can exit.
  trajectory_queue.BlockNewValues();
  trajectory_queue.Clear();

  std::cout << "Joining all the threads." << std::endl;
  for (auto& t : actors) {
    t.join();
  }
  for (auto& t : evaluators) {
    t.join();
  }
  std::cout << "Exiting cleanly." << std::endl;
  return true;
}

}  // namespace open_spiel::algorithms
