/* Copyright (c) 2008-2025 the MRtrix3 contributors.
 *
 * This Source Code Form is subject to the terms of the Mozilla Public
 * License, v. 2.0. If a copy of the MPL was not distributed with this
 * file, You can obtain one at http://mozilla.org/MPL/2.0/.
 *
 * Covered Software is provided under this License on an "as is"
 * basis, without warranty of any kind, either expressed, implied, or
 * statutory, including, without limitation, warranties that the
 * Covered Software is free of defects, merchantable, fit for a
 * particular purpose or non-infringing.
 * See the Mozilla Public License v. 2.0 for more details.
 *
 * For more details, see http://www.mrtrix.org/.
 */

#ifndef __algo_threaded_loop_h__
#define __algo_threaded_loop_h__

#include "debug.h"
#include "algo/loop.h"
#include "algo/iterator.h"
#include "thread.h"

namespace MR
{

  /** \addtogroup thread_classes
   * @{
   *
   * \defgroup image_thread_looping Thread-safe image looping
   *
   * These functions allows arbitrary looping operations to be performed in
   * parallel, using a versatile multi-threading framework. It works
   * hand-in-hand with the [single-threaded looping functions](@ref MR::Loop()),
   * and can be used to code up complex operations with relatively little
   * effort.
   *
   * The ThreadedLoop() function is generally used by first initialising
   * an object that will determine the order of traversal, which axes will be
   * looped over between synchronisation calls, and what message to display in
   * the progress bar if one is needed. This is all performed in the various
   * overloaded versions of ThreadedLoop(), which are template functions
   * allowing the class to be initialised from any `HeaderType` class. These are
   * described in more detail below.
   *
   * The object returned by the ThreadedLoop() functions provide methods to
   * loop over ImageType classes.  To do this, the object returned by
   * ThreadedLoop() keeps track of which axes will be managed by each thread
   * independently (the _inner axes_), and which will be coordinated across
   * threads (the _outer axes_).  By default, the inner axes consist of a
   * single axis, chosen to be the axis of smallest stride in the \a source
   * `HeaderType` class provided at initialisation. The remaining axes are
   * coordinated across threads: each invocation of the thread's functor is
   * given a fresh position to operate from, in the form of an Iterator class.
   *
   * To help illustrate the concept, consider an example whereby the inner axes
   * have been set to the x & y axes (i.e. axes 0 & 1), and the outer axes have
   * been set to the z and volume axes (i.e. axes 2 & 3). Each thread will do
   * the following:
   *
   * 1. lock the loop mutex, obtain a new set of z & volume coordinates, then
   *    release the mutex so other threads can obtain their own unique set of
   *    coordinates to process;
   * 2. set the position of all `ImageType` classes to be processed according
   *    to these coordinates;
   * 3. iterate over the x & y axes, invoking the user-supplied functor each
   *    time;
   * 4. repeat from step 1 until all the data have been processed.
   *
   *
   * \section threaded_loop_constructor Instantiating a ThreadedLoop() object
   *
   * The ThreadedLoop() functions can be used to set up any reasonable
   * multi-threaded looping structure. The various relevant aspects that can be
   * influenced include:
   *
   * - which axes will be iterated over; by default, all axes of the \a source
   *   `HeaderType` class will be included.
   *
   * - the order of traversal, in the form of a list of axes that will be
   *   iterated over in order; the first entry in the list will be iterated
   *   over in the inner-most loop, etc. By default, the axes are traversed in
   *   the order of increasing stride of the \a source InfoType class.
   *
   * - the number of axes that will be managed independently by each thread.
   *   By default, this number is one.
   *
   * - a string that will be displayed in the progress bar if one is desired.
   *
   * The various overloaded functions allow these settings to be specified in
   * different ways. A `HeaderType` class is always required; it is generally
   * recommended to provide the (or one of the) \e input `ImageType` class that
   * is to be processed, since its order of traversal will have the most
   * influence on performance (by making the most efficient use of the
   * hardware's RAM access and CPU cache). It is also possible to supply a
   * `vector<size_t>` directly if required.
   *
   * These axes can be restricted to a specific range to allow volume-wise
   * processing, etc.  The inner axes can be specified by supplying how many
   * are needed; they will then be taken from the list of axes to be looped
   * over, in order of increasing stride. It is also possible to provide the
   * list of inners axes and outer axes as separate `vector<size_t>`
   * arguments.
   *
   *
   *
   * \section threaded_loop_run Running the ThreadedLoop() objects
   *
   * The run() methods will run the ThreadedLoop, invoking the specified
   * function or functor once per voxel in the loop. The different versions
   * will expect different signatures for the function or functor, and manage
   * looping over different numbers of `ImageType` classes. This should be
   * clarified by examining the examples below.
   *
   * This example can be used to compute the exponential of the voxel values
   * in-place, while displaying a progress message:
   *
   * ~~~{.cpp}
   * void my_function (MyImageType& vox) {
   *   vox.value() = std::exp (vox.value());
   * }
   *
   * ...
   *
   * MyImageType vox;
   * ThreadedLoop ("computing exponential in-place", vox)
   *   .run (my_function, vox);
   * ~~~
   *
   * To make this operation more easily generalisable to any `ImageType`, use a
   * functor with a template operator() method:
   *
   * ~~~{.cpp}
   * class MyFunction {
   *   public:
   *     template <class ImageType>
   *       void operator() (ImageType& vox) {
   *         vox.value() = std::exp (vox.value());
   *       }
   * };
   *
   * ...
   *
   * AnyImageType vox;
   * ThreadedLoop ("computing exponential in-place", vox)
   *   .run (MyFunction(), vox);
   * ~~~
   *
   * Note that a simple operation such as the previous example can be written
   * more compactly using C++11 lambda expressions:
   * ~~~{.cpp}
   * MyImageType vox;
   * ThreadedLoop ("computing exponential in-place", vox)
   *   .run ([](decltype(vox)& v) { v.value() = std::exp(v.value()); }, vox);
   * ~~~
   *
   *
   * As a further example, the following snippet performs the addition of any
   * `ImageTypes` \a vox1 and \a vox2, this time storing the results in \a
   * vox_out, with no progress display, and looping according to the strides
   * in \a vox1:
   * ~~~{.cpp}
   * struct MyAdd {
   *   template <class ImageType1, class ImageType2, class ImageType3>
   *     void operator() (ImageType1& out, const ImageType2& in1, const ImageType3& in2) {
   *       out.value() = in1.value() + in2.value();
   *     }
   * };
   *
   * ...
   *
   * ThreadedLoop (vox1).run (MyAdd(), vox_out, vox1, vox2);
   * ~~~
   *
   * Note that the `ImageType` arguments to run() must be provided in the same
   * order as expected by the Functor's `operator()` method.
   *
   * Again, such a simple operation can be written more compactly using C++11
   * lambda expressions:
   * ~~~{.cpp}
   * auto f = [](decltype(vox_out)& out, decltype(vox1)& in1, decltype(vox2)& in2) {
   *   out.value() = in1.value() + in2.value();
   * }
   * ThreadedLoop (vox1).run (f, vox_out, vox1, vox2);
   * ~~~
   *
   *
   * This example uses a functor to computes the root-mean-square of \a vox:
   * \code
   * class RMS {
   *   public:
   *     // We pass a reference to the same double to all threads.
   *     // Each thread accumulates its own sum-of-squares, and
   *     // updates the overal sum-of-squares in the destructor, which is
   *     // guaranteed to be invoked after all threads have re-joined,
   *     // thereby avoiding race conditions.
   *     RMS (double& grand_SoS) : SoS (0.0), grand_SoS (grand_SoS) { }
   *     ~RMS () { grand_SoS += SoS; }
   *
   *     // accumulate the thread-local sum-of-squares:
   *     template <class ImageType>
   *       void operator() (const ImageType& in) {
   *         SoS += Math::pow2 (in.value());
   *       }
   *
   *   protected:
   *     double SoS;
   *     double& grand_SoS;
   * };
   *
   * ...
   *
   * double SoS = 0.0;
   * ThreadedLoop ("computing RMS of \"" + vox.name() + "\"", vox)
   *     .run (RMS(SoS), vox);
   *
   * double rms = std::sqrt (SoS / voxel_count (vox));
   * \endcode
   *
   * \section threaded_loop_run_outer The run_outer() method
   *
   * The run_outer() method can be used if needed to loop over the indices in
   * the outer loop only. This method is used internally by the run() methods,
   * and may prove useful in selected cases if each thread needs to handle its
   * own looping over the inner axes. Usage is essentially identical to the
   * run() method, and the function or functor provided will need to have a
   * void operator() (const Iterator& pos) method defined. Note that in this
   * case, any `ImageType` classes to be looped over will need to be stored as
   * members to the functor, since it will only receive an `Iterator` for each
   * invocation - the functor will need to then implement looping over the
   * inner axes from the position provided in the `Iterator`.
   *
   * \sa Loop
   * \sa Thread::run()
   * \sa thread_queue
   *
   * @}
   */



  namespace {

    inline vector<size_t> get_inner_axes (const vector<size_t>& axes, size_t num_inner_axes) {
      return { axes.begin(), axes.begin()+num_inner_axes };
    }

    inline vector<size_t> get_outer_axes (const vector<size_t>& axes, size_t num_inner_axes) {
      return { axes.begin()+num_inner_axes, axes.end() };
    }

    template <class HeaderType>
      inline vector<size_t> get_inner_axes (const HeaderType& source, size_t num_inner_axes, size_t from_axis, size_t to_axis) {
        return get_inner_axes (Stride::order (source, from_axis, to_axis), num_inner_axes);
      }

    template <class HeaderType>
      inline vector<size_t> get_outer_axes (const HeaderType& source, size_t num_inner_axes, size_t from_axis, size_t to_axis) {
        return get_outer_axes (Stride::order (source, from_axis, to_axis), num_inner_axes);
      }


    template <int N, class Functor, class... ImageType>
      struct ThreadedLoopRunInner
      { MEMALIGN(ThreadedLoopRunInner<N,Functor,ImageType...>)
        const vector<size_t>& outer_axes;
        decltype (Loop (outer_axes)) loop;
        typename std::remove_reference<Functor>::type func;
        std::tuple<ImageType...> vox;

        ThreadedLoopRunInner (const vector<size_t>& outer_axes, const vector<size_t>& inner_axes,
            const Functor& functor, ImageType&... voxels) :
          outer_axes (outer_axes),
          loop (Loop (inner_axes)),
          func (functor),
          vox (voxels...) { }

        void operator() (const Iterator& pos) {
          assign_pos_of (pos, outer_axes).to (vox);
          for (auto i = unpack (loop, vox); i; ++i)
            unpack (func, vox);
        }
      };


    template <class Functor, class... ImageType>
      struct ThreadedLoopRunInner<0,Functor,ImageType...>
      { MEMALIGN(ThreadedLoopRunInner<0, Functor,ImageType...>)
        const vector<size_t>& outer_axes;
        decltype (Loop (outer_axes)) loop;
        typename std::remove_reference<Functor>::type func;

        ThreadedLoopRunInner (const vector<size_t>& outer_axes, const vector<size_t>& inner_axes,
            const Functor& functor, ImageType&... /*voxels*/) :
          outer_axes (outer_axes),
          loop (Loop (inner_axes)),
          func (functor) { }

        void operator() (Iterator& pos) {
          for (auto i = loop (pos); i; ++i)
            func (pos);
        }
      };


      inline void __manage_progress (...) { }
      template <class LoopType, class ThreadType>
        inline auto __manage_progress (const LoopType* loop, const ThreadType* threads)
        -> decltype((void) (&loop->progress), void())
      {
        loop->progress.run_update_thread (*threads);
      }


    template <class OuterLoopType>
      struct ThreadedLoopRunOuter { MEMALIGN(ThreadedLoopRunOuter<OuterLoopType>)
        Iterator iterator;
        OuterLoopType outer_loop;
        vector<size_t> inner_axes;

        //! invoke \a functor (const Iterator& pos) per voxel <em> in the outer axes only</em>
        template <class Functor>
          void run_outer (Functor&& functor)
          {
            if (Thread::threads_to_execute() == 0) {
              for (auto i = outer_loop (iterator); i; ++i)
                functor (iterator);
              return;
            }

            std::mutex mutex;
            ProgressBar::SwitchToMultiThreaded progress_functions;

            struct Shared { MEMALIGN(Shared)
              Iterator& iterator;
              decltype (outer_loop (iterator)) loop;
              std::mutex& mutex;
              FORCE_INLINE bool next (Iterator& pos) {
                std::lock_guard<std::mutex> lock (mutex);
                if (loop) {
                  assign_pos_of (iterator, loop.axes).to (pos);
                  ++loop;
                  return true;
                }
                else return false;
              }
            } shared = { iterator, outer_loop (iterator), mutex };

            struct PerThread { MEMALIGN(PerThread)
              Shared& shared;
              Iterator pos;
              typename std::remove_reference<Functor>::type func;
              void execute () {
                while (shared.next (pos))
                  func (pos);
              }
            } loop_thread = { shared, shared.iterator, functor };

            auto threads = Thread::run (Thread::multi (loop_thread), "loop threads");

            __manage_progress (&shared.loop, &threads);
            threads.wait();
          }



        //! invoke \a functor (const Iterator& pos) per voxel <em> in the outer axes only</em>
        template <class Functor, class... ImageType>
          void run (Functor&& functor, ImageType&&... vox)
          {
            ThreadedLoopRunInner<
              sizeof...(ImageType),
              typename std::remove_reference<Functor>::type,
              typename std::remove_reference<ImageType>::type...
                > loop_thread (outer_loop.axes, inner_axes, functor, vox...);
            run_outer (loop_thread);
            check_app_exit_code();
          }

      };
  }







  //! Multi-threaded loop object
  //* \sa image_thread_looping for details */
  template <class HeaderType>
    inline ThreadedLoopRunOuter<decltype(Loop(vector<size_t>()))>
    ThreadedLoop (
        const HeaderType& source,
        const vector<size_t>& outer_axes,
        const vector<size_t>& inner_axes)
    { return { source, Loop (outer_axes), inner_axes }; }


  //! Multi-threaded loop object
  //* \sa image_thread_looping for details */
  template <class HeaderType>
    inline ThreadedLoopRunOuter<decltype(Loop(vector<size_t>()))>
    ThreadedLoop (
        const HeaderType& source,
        const vector<size_t>& axes,
        size_t num_inner_axes = 1)
    { return { source, Loop (get_outer_axes (axes, num_inner_axes)), get_inner_axes (axes, num_inner_axes) }; }

  //! Multi-threaded loop object
  //* \sa image_thread_looping for details */
  template <class HeaderType>
    inline ThreadedLoopRunOuter<decltype(Loop(vector<size_t>()))>
    ThreadedLoop (
        const HeaderType& source,
        size_t from_axis = 0,
        size_t to_axis = std::numeric_limits<size_t>::max(),
        size_t num_inner_axes = 1)
    {
      return { source,
        Loop (get_outer_axes (source, num_inner_axes, from_axis, to_axis)),
        get_inner_axes (source, num_inner_axes, from_axis, to_axis) };
    }

  //! Multi-threaded loop object
  //* \sa image_thread_looping for details */
  template <class HeaderType>
    inline ThreadedLoopRunOuter<decltype(Loop("", vector<size_t>()))>
    ThreadedLoop (
        const std::string& progress_message,
        const HeaderType& source,
        const vector<size_t>& outer_axes,
        const vector<size_t>& inner_axes)
    { return { source, Loop (progress_message, outer_axes), inner_axes }; }

  //! Multi-threaded loop object
  //* \sa image_thread_looping for details */
  template <class HeaderType>
    inline ThreadedLoopRunOuter<decltype(Loop("", vector<size_t>()))>
    ThreadedLoop (
        const std::string& progress_message,
        const HeaderType& source,
        const vector<size_t>& axes,
        size_t num_inner_axes = 1)
    {
      return { source,
        Loop (progress_message, get_outer_axes (axes, num_inner_axes)),
        get_inner_axes (axes, num_inner_axes) };
    }

  //! Multi-threaded loop object
  //* \sa image_thread_looping for details */
  template <class HeaderType>
    inline ThreadedLoopRunOuter<decltype(Loop("", vector<size_t>()))>
    ThreadedLoop (
        const std::string& progress_message,
        const HeaderType& source,
        size_t from_axis = 0,
        size_t to_axis = std::numeric_limits<size_t>::max(),
        size_t num_inner_axes = 1)
    {
      return { source,
        Loop (progress_message, get_outer_axes (source, num_inner_axes, from_axis, to_axis)),
        get_inner_axes (source, num_inner_axes, from_axis, to_axis) };
    }



}

#endif


