////////////////////////////////////////////////////////////////////////////////
// Copyright (c) 2014-2021, Lawrence Livermore National Security, LLC.
// Produced at the Lawrence Livermore National Laboratory.
// Written by the LBANN Research Team (B. Van Essen, et al.) listed in
// the CONTRIBUTORS file. <lbann-dev@llnl.gov>
//
// LLNL-CODE-697807.
// All rights reserved.
//
// This file is part of LBANN: Livermore Big Artificial Neural Network
// Toolkit. For details, see http://software.llnl.gov/LBANN or
// https://github.com/LLNL/LBANN.
//
// Licensed under the Apache License, Version 2.0 (the "Licensee"); 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.
//
// lbann_proto.cpp - prototext application
////////////////////////////////////////////////////////////////////////////////

#include "lbann/lbann.hpp"
#include "lbann/proto/proto_common.hpp"
#include "lbann/utils/argument_parser.hpp"
#include "lbann/utils/protobuf_utils.hpp"

#include "lbann/proto/lbann.pb.h"
#include "lbann/proto/model.pb.h"

#include <cstdlib>

using namespace lbann;

namespace {
int guess_global_rank() noexcept
{
  int have_mpi;
  MPI_Initialized(&have_mpi);
  if (have_mpi) {
    int rank;
    MPI_Comm_rank(MPI_COMM_WORLD, &rank);
    return rank;
  }
  else {
    if (char const* slurm_flag = std::getenv("SLURM_PROCID"))
      return std::stoi(slurm_flag);
    if (char const* open_mpi_flag = std::getenv("OMPI_WORLD_COMM_RANK"))
      return std::stoi(open_mpi_flag);
    else if (char const* mv2_flag = std::getenv("MV2_COMM_WORLD_LOCAL_RANK"))
      return std::stoi(mv2_flag);
    else
      return -1;
  }
}
} // namespace

int main(int argc, char* argv[])
{
  auto& arg_parser = global_argument_parser();
  construct_all_options();

  try {
    arg_parser.parse(argc, argv);
  }
  catch (std::exception const& e) {
    auto guessed_rank = guess_global_rank();
    if (guessed_rank <= 0)
      // Cannot call `El::ReportException` because MPI hasn't been
      // initialized yet.
      std::cerr << "Error during argument parsing:\n\ne.what():\n\n  "
                << e.what() << "\n\nProcess terminating." << std::endl;
    std::terminate();
  }

  world_comm_ptr comm = initialize(argc, argv);
  const bool master = comm->am_world_master();

  try {
    // Split MPI into trainers
    allocate_trainer_resources(comm.get());

    if (arg_parser.help_requested() or argc == 1) {
      if (master)
        std::cout << arg_parser << std::endl;
      return EXIT_SUCCESS;
    }

    std::ostringstream err;

    auto pbs = protobuf_utils::load_prototext(master);
    // Optionally over-ride some values in the prototext for each model
    for (size_t i = 0; i < pbs.size(); i++) {
      get_cmdline_overrides(*comm, *(pbs[i]));
    }

    lbann_data::LbannPB& pb = *(pbs[0]);
    lbann_data::Trainer* pb_trainer = pb.mutable_trainer();

    // Construct the trainer
    auto& trainer = construct_trainer(comm.get(), pb_trainer, *(pbs[0]));

    thread_pool& io_thread_pool = trainer.get_io_thread_pool();

    int training_dr_linearized_data_size = -1;
    auto* dr =
      trainer.get_data_coordinator().get_data_reader(execution_mode::training);
    if (dr != nullptr) {
      training_dr_linearized_data_size = dr->get_linearized_data_size();
    }

    auto model_1 = build_model_from_prototext(
      argc,
      argv,
      pb_trainer,
      *(pbs[0]),
      comm.get(),
      io_thread_pool,
      trainer.get_callbacks_with_ownership(),
      training_dr_linearized_data_size);      // discriminator model
    std::unique_ptr<model> model_2 = nullptr; // adversarial model
    if (pbs.size() > 1) {
      model_2 =
        build_model_from_prototext(argc,
                                   argv,
                                   pb_trainer,
                                   *(pbs[1]),
                                   comm.get(),
                                   io_thread_pool,
                                   trainer.get_callbacks_with_ownership(),
                                   training_dr_linearized_data_size);
    }

    const lbann_data::Model pb_model = pbs[0]->model();
    const lbann_data::Model pb_model_2 = pbs[1]->model();

    const auto layers1 = model_1->get_layers();
    const auto layers2 = model_2->get_layers();
    int super_step = 1;
    int max_super_step = pb_model.super_steps();
    while (super_step <= max_super_step) {
      if (master)
        std::cerr << "\nSTARTING train - discriminator model at step "
                  << super_step << "\n\n";
      //@todo freeze generator layers in this step
      trainer.train(model_1.get(), super_step * pb_model.num_epochs());

      // Replace/copy "proxy" layer in adversarial model (model2) with its
      // "equivalent" layer in discriminator model (model1)
      //@todo freeze layers after replacement
      for (size_t l2 = 0; l2 < layers2.size(); l2++) {
        // check if a discriminator layer is a proxy
        std::string l2_fullname = layers2[l2]->get_name();
        if (l2_fullname.find("proxy") != std::string::npos) { // if a proxy
                                                              // layer
          std::string l2_name = l2_fullname.erase(l2_fullname.length() - 6);
          std::cout << "L2 Name " << l2_name << std::endl;
          for (size_t l1 = 0; l1 < layers1.size(); l1++) {
            if (l2_name == layers1[l1]->get_name()) {
              if (master)
                std::cout << "Replacing adversarial model (model 2) Layer "
                          << layers1[l1]->get_name();
              layers2[l2]->replace_weights(*layers1[l1]);
              if (master)
                std::cout << " with corresponding layer "
                          << layers2[l2]->get_name()
                          << " in discriminator model (model1) " << std::endl;
            }
          }
        }
      }

      if (master)
        std::cerr << "\n STARTING train - adversarial model at step "
                  << super_step << " \n\n";
      trainer.train(model_2.get(), super_step * pb_model_2.num_epochs());

      super_step++;
    }
  }
  catch (std::exception& e) {
    El::ReportException(e);
    // It's possible that a proper subset of ranks throw some
    // exception. But we want to tear down the whole world.
    El::mpi::Abort(El::mpi::COMM_WORLD, EXIT_FAILURE);
  }

  return EXIT_SUCCESS;
}
