#ifndef MLPP_TENSOR3_H
#define MLPP_TENSOR3_H

/*************************************************************************/
/*  mlpp_tensor3.h                                                       */
/*************************************************************************/
/*                         This file is part of:                         */
/*                    PMLPP Machine Learning Library                     */
/*                   https://github.com/Relintai/pmlpp                   */
/*************************************************************************/
/* Copyright (c) 2023-present Péter Magyar.                              */
/* Copyright (c) 2022-2023 Marc Melikyan                                 */
/*                                                                       */
/* Permission is hereby granted, free of charge, to any person obtaining */
/* a copy of this software and associated documentation files (the       */
/* "Software"), to deal in the Software without restriction, including   */
/* without limitation the rights to use, copy, modify, merge, publish,   */
/* distribute, sublicense, and/or sell copies of the Software, and to    */
/* permit persons to whom the Software is furnished to do so, subject to */
/* the following conditions:                                             */
/*                                                                       */
/* The above copyright notice and this permission notice shall be        */
/* included in all copies or substantial portions of the Software.       */
/*                                                                       */
/* THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,       */
/* EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF    */
/* MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.*/
/* IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY  */
/* CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,  */
/* TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE     */
/* SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.                */
/*************************************************************************/

#ifdef USING_SFW
#include "sfw.h"
#else

#include "core/math/math_defs.h"

#include "core/containers/pool_vector.h"
#include "core/containers/sort_array.h"
#include "core/containers/vector.h"
#include "core/error/error_macros.h"
#include "core/math/vector2i.h"
#include "core/os/memory.h"

#include "core/object/resource.h"
#endif

#include "mlpp_matrix.h"
#include "mlpp_vector.h"

class Image;

class MLPPTensor3 : public Resource {
  GDCLASS(MLPPTensor3, Resource);

public:
  Array get_data();
  void set_data(const Array &p_from);

  _FORCE_INLINE_ real_t *ptrw() { return _data; }

  _FORCE_INLINE_ const real_t *ptr() const { return _data; }

  void z_slice_add(const Vector<real_t> &p_row);
  void z_slice_add_pool_vector(const PoolRealArray &p_row);
  void z_slice_add_mlpp_vector(const Ref<MLPPVector> &p_row);
  void z_slice_add_mlpp_matrix(const Ref<MLPPMatrix> &p_matrix);
  void z_slice_remove(int p_index);

  // Removes the item copying the last value into the position of the one to
  // remove. It's generally faster than `remove`.
  void z_slice_remove_unordered(int p_index);

  void z_slice_swap(int p_index_1, int p_index_2);

  _FORCE_INLINE_ void clear() { resize(Size3i()); }
  _FORCE_INLINE_ void reset() {
    if (_data) {
      memfree(_data);
      _data = NULL;
      _size = Size3i();
    }
  }

  _FORCE_INLINE_ bool empty() const { return _size == Size3i(); }
  _FORCE_INLINE_ int z_slice_data_size() const { return _size.x * _size.y; }
  _FORCE_INLINE_ Size2i z_slice_size() const {
    return Size2i(_size.x, _size.y);
  }
  _FORCE_INLINE_ int data_size() const { return _size.x * _size.y * _size.z; }
  _FORCE_INLINE_ Size3i size() const { return _size; }

  void resize(const Size3i &p_size);
  void shape_set(const Size3i &p_size);

  _FORCE_INLINE_ int calculate_index(int p_index_z, int p_index_y,
                                     int p_index_x) const {
    return p_index_y * _size.x + p_index_x + _size.x * _size.y * p_index_z;
  }

  _FORCE_INLINE_ int calculate_z_slice_index(int p_index_z) const {
    return _size.x * _size.y * p_index_z;
  }

  _FORCE_INLINE_ const real_t &operator[](int p_index) const {
    CRASH_BAD_INDEX(p_index, data_size());
    return _data[p_index];
  }
  _FORCE_INLINE_ real_t &operator[](int p_index) {
    CRASH_BAD_INDEX(p_index, data_size());
    return _data[p_index];
  }

  _FORCE_INLINE_ real_t element_get_index(int p_index) const {
    ERR_FAIL_INDEX_V(p_index, data_size(), 0);

    return _data[p_index];
  }

  _FORCE_INLINE_ void element_set_index(int p_index, real_t p_val) {
    ERR_FAIL_INDEX(p_index, data_size());

    _data[p_index] = p_val;
  }

  _FORCE_INLINE_ real_t element_get(int p_index_z, int p_index_y,
                                    int p_index_x) const {
    ERR_FAIL_INDEX_V(p_index_x, _size.x, 0);
    ERR_FAIL_INDEX_V(p_index_y, _size.y, 0);
    ERR_FAIL_INDEX_V(p_index_z, _size.z, 0);

    return _data[p_index_y * _size.x + p_index_x +
                 _size.x * _size.y * p_index_z];
  }

  _FORCE_INLINE_ void element_set(int p_index_z, int p_index_y, int p_index_x,
                                  real_t p_val) {
    ERR_FAIL_INDEX(p_index_x, _size.x);
    ERR_FAIL_INDEX(p_index_y, _size.y);
    ERR_FAIL_INDEX(p_index_z, _size.z);

    _data[p_index_y * _size.x + p_index_x + _size.x * _size.y * p_index_z] =
        p_val;
  }

  Vector<real_t> row_get_vector(int p_index_z, int p_index_y) const;
  PoolRealArray row_get_pool_vector(int p_index_z, int p_index_y) const;
  Ref<MLPPVector> row_get_mlpp_vector(int p_index_z, int p_index_y) const;
  void row_get_into_mlpp_vector(int p_index_z, int p_index_y,
                                Ref<MLPPVector> target) const;

  void row_set_vector(int p_index_z, int p_index_y,
                      const Vector<real_t> &p_row);
  void row_set_pool_vector(int p_index_z, int p_index_y,
                           const PoolRealArray &p_row);
  void row_set_mlpp_vector(int p_index_z, int p_index_y,
                           const Ref<MLPPVector> &p_row);

  Vector<real_t> z_slice_get_vector(int p_index_z) const;
  PoolRealArray z_slice_get_pool_vector(int p_index_z) const;
  Ref<MLPPVector> z_slice_get_mlpp_vector(int p_index_z) const;
  void z_slice_get_into_mlpp_vector(int p_index_z,
                                    Ref<MLPPVector> target) const;
  Ref<MLPPMatrix> z_slice_get_mlpp_matrix(int p_index_z) const;
  void z_slice_get_into_mlpp_matrix(int p_index_z,
                                    Ref<MLPPMatrix> target) const;

  void z_slice_set_vector(int p_index_z, const Vector<real_t> &p_row);
  void z_slice_set_pool_vector(int p_index_z, const PoolRealArray &p_row);
  void z_slice_set_mlpp_vector(int p_index_z, const Ref<MLPPVector> &p_row);
  void z_slice_set_mlpp_matrix(int p_index_z, const Ref<MLPPMatrix> &p_mat);

  // TODO resize() need to be reworked for add and remove to work, in any other
  // direction than z void x_slice_add(const Ref<MLPPMatrix> &p_matrix); void
  // x_slice_remove(int p_index);
  void x_slice_get_into(int p_index_x, Ref<MLPPMatrix> target) const;
  Ref<MLPPMatrix> x_slice_get(int p_index_x) const;
  void x_slice_set(int p_index_x, const Ref<MLPPMatrix> &p_mat);

  // void y_slice_add(const Ref<MLPPMatrix> &p_matrix);
  // void y_slice_remove(int p_index);
  void y_slice_get_into(int p_index_y, Ref<MLPPMatrix> target) const;
  Ref<MLPPMatrix> y_slice_get(int p_index_y) const;
  void y_slice_set(int p_index_y, const Ref<MLPPMatrix> &p_mat);

public:
  // Image api

  enum ImageChannelFlags {
    IMAGE_CHANNEL_FLAG_R = 1 << 0,
    IMAGE_CHANNEL_FLAG_G = 1 << 1,
    IMAGE_CHANNEL_FLAG_B = 1 << 2,
    IMAGE_CHANNEL_FLAG_A = 1 << 3,

    IMAGE_CHANNEL_FLAG_NONE = 0,
    IMAGE_CHANNEL_FLAG_RG = IMAGE_CHANNEL_FLAG_R | IMAGE_CHANNEL_FLAG_G,
    IMAGE_CHANNEL_FLAG_RGB =
        IMAGE_CHANNEL_FLAG_R | IMAGE_CHANNEL_FLAG_G | IMAGE_CHANNEL_FLAG_B,
    IMAGE_CHANNEL_FLAG_GB = IMAGE_CHANNEL_FLAG_G | IMAGE_CHANNEL_FLAG_B,
    IMAGE_CHANNEL_FLAG_GBA =
        IMAGE_CHANNEL_FLAG_G | IMAGE_CHANNEL_FLAG_B | IMAGE_CHANNEL_FLAG_A,
    IMAGE_CHANNEL_FLAG_BA = IMAGE_CHANNEL_FLAG_B | IMAGE_CHANNEL_FLAG_A,
    IMAGE_CHANNEL_FLAG_RGBA = IMAGE_CHANNEL_FLAG_R | IMAGE_CHANNEL_FLAG_G |
                              IMAGE_CHANNEL_FLAG_B | IMAGE_CHANNEL_FLAG_A,
  };

  void z_slices_add_image(const Ref<Image> &p_img,
                          const int p_channels = IMAGE_CHANNEL_FLAG_RGBA);

  Ref<Image> z_slice_get_image(const int p_index_z) const;
  Ref<Image> z_slices_get_image(const int p_index_r = -1,
                                const int p_index_g = -1,
                                const int p_index_b = -1,
                                const int p_index_a = -1) const;

  void z_slice_get_into_image(
      Ref<Image> p_target, const int p_index_z,
      const int p_target_channels = IMAGE_CHANNEL_FLAG_RGB) const;
  void z_slices_get_into_image(Ref<Image> p_target, const int p_index_r = -1,
                               const int p_index_g = -1,
                               const int p_index_b = -1,
                               const int p_index_a = -1) const;

  void z_slice_set_image(const Ref<Image> &p_img, const int p_index_z,
                         const int p_image_channel_flag = IMAGE_CHANNEL_FLAG_R);
  void z_slices_set_image(const Ref<Image> &p_img, const int p_index_r = -1,
                          const int p_index_g = -1, const int p_index_b = -1,
                          const int p_index_a = -1);

  void set_from_image(const Ref<Image> &p_img,
                      const int p_channels = IMAGE_CHANNEL_FLAG_RGBA);

  // void x_slices_add_image(const Ref<Image> &p_img, const int p_channels =
  // IMAGE_CHANNEL_FLAG_RGBA);
  Ref<Image> x_slice_get_image(const int p_index_x) const;
  void x_slice_get_into_image(
      Ref<Image> p_target, const int p_index_x,
      const int p_target_channels = IMAGE_CHANNEL_FLAG_RGB) const;
  void x_slice_set_image(const Ref<Image> &p_img, const int p_index_x,
                         const int p_image_channel_flag = IMAGE_CHANNEL_FLAG_R);

  // void y_slices_add_image(const Ref<Image> &p_img, const int p_channels =
  // IMAGE_CHANNEL_FLAG_RGBA);
  Ref<Image> y_slice_get_image(const int p_index_y) const;
  void y_slice_get_into_image(
      Ref<Image> p_target, const int p_index_y,
      const int p_target_channels = IMAGE_CHANNEL_FLAG_RGB) const;
  void y_slice_set_image(const Ref<Image> &p_img, const int p_index_y,
                         const int p_image_channel_flag = IMAGE_CHANNEL_FLAG_R);

public:
  // math api

  void add(const Ref<MLPPTensor3> &B);
  Ref<MLPPTensor3> addn(const Ref<MLPPTensor3> &B) const;
  void addb(const Ref<MLPPTensor3> &A, const Ref<MLPPTensor3> &B);

  void sub(const Ref<MLPPTensor3> &B);
  Ref<MLPPTensor3> subn(const Ref<MLPPTensor3> &B) const;
  void subb(const Ref<MLPPTensor3> &A, const Ref<MLPPTensor3> &B);

  void division_element_wise(const Ref<MLPPTensor3> &B);
  Ref<MLPPTensor3> division_element_wisen(const Ref<MLPPTensor3> &B) const;
  void division_element_wiseb(const Ref<MLPPTensor3> &A,
                              const Ref<MLPPTensor3> &B);

  void sqrt();
  Ref<MLPPTensor3> sqrtn() const;
  void sqrtb(const Ref<MLPPTensor3> &A);

  void exponentiate(real_t p);
  Ref<MLPPTensor3> exponentiaten(real_t p) const;
  void exponentiateb(const Ref<MLPPTensor3> &A, real_t p);

  void scalar_multiply(const real_t scalar);
  Ref<MLPPTensor3> scalar_multiplyn(const real_t scalar) const;
  void scalar_multiplyb(const real_t scalar, const Ref<MLPPTensor3> &A);

  void scalar_add(const real_t scalar);
  Ref<MLPPTensor3> scalar_addn(const real_t scalar) const;
  void scalar_addb(const real_t scalar, const Ref<MLPPTensor3> &A);

  void hadamard_product(const Ref<MLPPTensor3> &B);
  Ref<MLPPTensor3> hadamard_productn(const Ref<MLPPTensor3> &B) const;
  void hadamard_productb(const Ref<MLPPTensor3> &A, const Ref<MLPPTensor3> &B);

  void max(const Ref<MLPPTensor3> &B);
  Ref<MLPPTensor3> maxn(const Ref<MLPPTensor3> &B) const;
  void maxb(const Ref<MLPPTensor3> &A, const Ref<MLPPTensor3> &B);

  void abs();
  Ref<MLPPTensor3> absn() const;
  void absb(const Ref<MLPPTensor3> &A);

  Ref<MLPPVector> flatten() const;
  void flatteno(Ref<MLPPVector> out) const;

  // real_t norm_2(std::vector<std::vector<std::vector<real_t>>> A);

  Ref<MLPPMatrix> tensor_vec_mult(const Ref<MLPPVector> &b);
  // std::vector<std::vector<std::vector<real_t>>>
  // vector_wise_tensor_product(std::vector<std::vector<std::vector<real_t>>> A,
  // std::vector<std::vector<real_t>> B);

public:
  void fill(real_t p_val);

  Vector<real_t> to_flat_vector() const;
  PoolRealArray to_flat_pool_vector() const;
  Vector<uint8_t> to_flat_byte_array() const;

  Ref<MLPPTensor3> duplicate_fast() const;

  void set_from_mlpp_tensor3(const Ref<MLPPTensor3> &p_from);
  void set_from_mlpp_tensor3r(const MLPPTensor3 &p_from);

  void set_from_mlpp_matrix(const Ref<MLPPMatrix> &p_from);
  void set_from_mlpp_matrixr(const MLPPMatrix &p_from);
  void set_from_mlpp_vectors(const Vector<Ref<MLPPVector>> &p_from);
  void set_from_mlpp_matricess(const Vector<Ref<MLPPMatrix>> &p_from);

  void set_from_mlpp_vectors_array(const Array &p_from);
  void set_from_mlpp_matrices_array(const Array &p_from);

  bool
  is_equal_approx(const Ref<MLPPTensor3> &p_with,
                  real_t tolerance = static_cast<real_t>(CMP_EPSILON)) const;

  String to_string();

  MLPPTensor3();
  MLPPTensor3(const MLPPMatrix &p_from);
  MLPPTensor3(const Array &p_from);
  ~MLPPTensor3();

  // TODO: These are temporary
  std::vector<real_t> to_flat_std_vector() const;
  void set_from_std_vectors(
      const std::vector<std::vector<std::vector<real_t>>> &p_from);
  std::vector<std::vector<std::vector<real_t>>> to_std_vector();
  MLPPTensor3(const std::vector<std::vector<std::vector<real_t>>> &p_from);

protected:
  static void _bind_methods();

protected:
  Size3i _size;
  real_t *_data;
};

VARIANT_ENUM_CAST(MLPPTensor3::ImageChannelFlags);

#endif