#pragma once

#include "launch.hpp"

namespace tf {

// Function: make_transform_task
template <
  typename B, typename E, typename O, typename C, typename P = DefaultPartitioner,
  std::enable_if_t<is_partitioner_v<std::decay_t<P>>, void>* = nullptr
>
auto make_transform_task(B first1, E last1, O d_first, C c, P part = P()) {

  using B_t = std::decay_t<unwrap_ref_decay_t<B>>;
  using E_t = std::decay_t<unwrap_ref_decay_t<E>>;
  using O_t = std::decay_t<unwrap_ref_decay_t<O>>;
  
  return [=] (Runtime& rt) mutable {

    // fetch the stateful values
    B_t beg   = first1;
    E_t end   = last1;
    O_t d_beg = d_first;

    size_t W = rt.executor().num_workers();
    size_t N = std::distance(beg, end);

    // only myself - no need to spawn another graph
    if(W <= 1 || N <= part.chunk_size()) {
      launch_loop(part, [&](){
        std::transform(beg, end, d_beg, c);
      });
      return;
    }

    if(N < W) {
      W = N;
    }

    // static partitioner
    if constexpr(part.type() == PartitionerType::STATIC) {
      size_t chunk_size;
      for(size_t w=0, curr_b=0; w<W && curr_b < N; ++w, curr_b += chunk_size) {
        chunk_size = part.adjusted_chunk_size(N, W, w);
        launch_loop(W, w, rt, part, [=, &part] () mutable {
          part.loop(N, W, curr_b, chunk_size,
            [&, prev_e=size_t{0}](size_t part_b, size_t part_e) mutable {
              std::advance(beg, part_b - prev_e);
              std::advance(d_beg, part_b - prev_e);
              for(size_t x = part_b; x<part_e; x++) {
                *d_beg++ = c(*beg++);
              }
              prev_e = part_e;
            }
          );
        });
      }
      rt.corun_all();
    }
    // dynamic partitioner
    else {
      std::atomic<size_t> next(0);
      launch_loop(N, W, rt, next, part, [=, &next, &part] () mutable {
        part.loop(N, W, next, 
          [&, prev_e=size_t{0}](size_t part_b, size_t part_e) mutable {
            std::advance(beg, part_b - prev_e);
            std::advance(d_beg, part_b - prev_e);
            for(size_t x = part_b; x<part_e; x++) {
              *d_beg++ = c(*beg++);
            }
            prev_e = part_e;
          }
        ); 
      });
    }
  };
}

// Function: make_transform_task
template <
  typename B1, typename E1, typename B2, typename O, typename C, typename P = DefaultPartitioner,
  std::enable_if_t<!is_partitioner_v<std::decay_t<C>>, void>* = nullptr
>
auto make_transform_task(B1 first1, E1 last1, B2 first2, O d_first, C c, P part = P()) {

  using B1_t = std::decay_t<unwrap_ref_decay_t<B1>>;
  using E1_t = std::decay_t<unwrap_ref_decay_t<E1>>;
  using B2_t = std::decay_t<unwrap_ref_decay_t<B2>>;
  using O_t = std::decay_t<unwrap_ref_decay_t<O>>;

  return [=] (Runtime& rt) mutable {

    // fetch the stateful values
    B1_t beg1 = first1;
    E1_t end1 = last1;
    B2_t beg2 = first2;
    O_t d_beg = d_first;

    size_t W = rt.executor().num_workers();
    size_t N = std::distance(beg1, end1);

    // only myself - no need to spawn another graph
    if(W <= 1 || N <= part.chunk_size()) {
      launch_loop(part, [&](){
        std::transform(beg1, end1, beg2, d_beg, c);
      });
      return;
    }

    if(N < W) {
      W = N;
    }

    // static partitioner
    if constexpr(part.type() == PartitionerType::STATIC) {
      size_t chunk_size;
      for(size_t w=0, curr_b=0; w<W && curr_b < N; ++w, curr_b += chunk_size) {
        chunk_size = part.adjusted_chunk_size(N, W, w);
        launch_loop(W, w, rt, part, [=, &c, &part] () mutable {
          part.loop(N, W, curr_b, chunk_size,
            [&, prev_e=size_t{0}](size_t part_b, size_t part_e) mutable {
              std::advance(beg1, part_b - prev_e);
              std::advance(beg2, part_b - prev_e);
              std::advance(d_beg, part_b - prev_e);
              for(size_t x = part_b; x<part_e; x++) {
                *d_beg++ = c(*beg1++, *beg2++);
              }
              prev_e = part_e;
            }
          );
        });
      }
      rt.corun_all();
    }
    // dynamic partitioner
    else {
      std::atomic<size_t> next(0);
      launch_loop(N, W, rt, next, part, [=, &c, &next, &part] () mutable {
        part.loop(N, W, next, 
          [&, prev_e=size_t{0}](size_t part_b, size_t part_e) mutable {
            std::advance(beg1, part_b - prev_e);
            std::advance(beg2, part_b - prev_e);
            std::advance(d_beg, part_b - prev_e);
            for(size_t x = part_b; x<part_e; x++) {
              *d_beg++ = c(*beg1++, *beg2++);
            }
            prev_e = part_e;
          }
        );
      });
    }
  };
}

// ----------------------------------------------------------------------------
// transform
// ----------------------------------------------------------------------------

// Function: transform
template <typename B, typename E, typename O, typename C, typename P,
  std::enable_if_t<is_partitioner_v<std::decay_t<P>>, void>*
>
Task FlowBuilder::transform(B first1, E last1, O d_first, C c, P part) {
  return emplace(
    make_transform_task(first1, last1, d_first, c, part)
  );
}

// ----------------------------------------------------------------------------
// transform2
// ----------------------------------------------------------------------------
  
// Function: transform
template <
  typename B1, typename E1, typename B2, typename O, typename C, typename P,
  std::enable_if_t<!is_partitioner_v<std::decay_t<C>>, void>*
>
Task FlowBuilder::transform(
  B1 first1, E1 last1, B2 first2, O d_first, C c, P part
) {
  return emplace(make_transform_task(
    first1, last1, first2, d_first, c, part
  ));
}


}  // end of namespace tf -----------------------------------------------------