// Copyright (c) by respective owners including Yahoo!, Microsoft, and
// individual contributors. All rights reserved. Released under a BSD (revised)
// license as described in the file LICENSE.

#include "vw/core/reductions/cb/cb_explore_adf_softmax.h"

#include "vw/config/options.h"
#include "vw/core/gen_cs_example.h"
#include "vw/core/global_data.h"
#include "vw/core/label_parser.h"
#include "vw/core/parser.h"
#include "vw/core/reductions/cb/cb_adf.h"
#include "vw/core/reductions/cb/cb_explore.h"
#include "vw/core/reductions/cb/cb_explore_adf_common.h"
#include "vw/core/setup_base.h"
#include "vw/explore/explore.h"

#include <algorithm>
#include <cmath>
#include <vector>
using namespace VW::cb_explore_adf;

namespace
{
class cb_explore_adf_softmax
{
public:
  cb_explore_adf_softmax(float epsilon, float lambda);
  ~cb_explore_adf_softmax() = default;

  // Should be called through cb_explore_adf_base for pre/post-processing
  void predict(VW::LEARNER::learner& base, VW::multi_ex& examples) { predict_or_learn_impl<false>(base, examples); }
  void learn(VW::LEARNER::learner& base, VW::multi_ex& examples) { predict_or_learn_impl<true>(base, examples); }

private:
  float _epsilon;
  float _lambda;
  template <bool is_learn>
  void predict_or_learn_impl(VW::LEARNER::learner& base, VW::multi_ex& examples);
};

cb_explore_adf_softmax::cb_explore_adf_softmax(float epsilon, float lambda) : _epsilon(epsilon), _lambda(lambda) {}

template <bool is_learn>
void cb_explore_adf_softmax::predict_or_learn_impl(VW::LEARNER::learner& base, VW::multi_ex& examples)
{
  VW::LEARNER::multiline_learn_or_predict<is_learn>(base, examples, examples[0]->ft_offset);

  VW::v_array<VW::action_score>& preds = examples[0]->pred.a_s;
  VW::explore::generate_softmax(
      -_lambda, begin_scores(preds), end_scores(preds), begin_scores(preds), end_scores(preds));

  VW::explore::enforce_minimum_probability(_epsilon, true, begin_scores(preds), end_scores(preds));
}
}  // namespace
std::shared_ptr<VW::LEARNER::learner> VW::reductions::cb_explore_adf_softmax_setup(VW::setup_base_i& stack_builder)
{
  VW::config::options_i& options = *stack_builder.get_options();
  VW::workspace& all = *stack_builder.get_all_pointer();
  using config::make_option;
  bool cb_explore_adf_option = false;
  bool softmax = false;
  float epsilon = 0.;
  float lambda = 0.;
  config::option_group_definition new_options("[Reduction] Contextual Bandit Exploration with ADF (softmax)");
  new_options
      .add(make_option("cb_explore_adf", cb_explore_adf_option)
               .keep()
               .necessary()
               .help("Online explore-exploit for a contextual bandit problem with multiline action dependent features"))
      .add(
          make_option("epsilon", epsilon).default_value(0.f).keep().allow_override().help("Epsilon-greedy exploration"))
      .add(make_option("softmax", softmax).keep().necessary().help("Softmax exploration"))
      .add(make_option("lambda", lambda).keep().allow_override().default_value(1.f).help("Parameter for softmax"));

  if (!options.add_parse_and_check_necessary(new_options)) { return nullptr; }

  if (lambda < 0)
  {  // Lambda should always be positive because we are using a cost basis.
    lambda = -lambda;
  }

  // Ensure serialization of cb_adf in all cases.
  if (!options.was_supplied("cb_adf")) { options.insert("cb_adf", ""); }

  // Set explore_type
  size_t feature_width = 1;

  auto base = require_multiline(stack_builder.setup_base_learner(feature_width));

  using explore_type = cb_explore_adf_base<cb_explore_adf_softmax>;
  auto data = VW::make_unique<explore_type>(all.output_runtime.global_metrics.are_metrics_enabled(), epsilon, lambda);

  if (epsilon < 0.0 || epsilon > 1.0) { THROW("The value of epsilon must be in [0,1]"); }
  auto l = make_reduction_learner(std::move(data), base, explore_type::learn, explore_type::predict,
      stack_builder.get_setupfn_name(cb_explore_adf_softmax_setup))
               .set_input_label_type(VW::label_type_t::CB)
               .set_output_label_type(VW::label_type_t::CB)
               .set_input_prediction_type(VW::prediction_type_t::ACTION_SCORES)
               .set_output_prediction_type(VW::prediction_type_t::ACTION_PROBS)
               .set_feature_width(feature_width)
               .set_output_example_prediction(explore_type::output_example_prediction)
               .set_update_stats(explore_type::update_stats)
               .set_print_update(explore_type::print_update)
               .set_persist_metrics(explore_type::persist_metrics)
               .build();
  return l;
}
