#pragma once

#include <stddef.h>
#include <stdint.h>
#include <stdbool.h>

#include <pthreadpool.h>

#ifdef __cplusplus
extern "C" {
#endif

/**
 * @brief Status code for any NNPACK function call.
 */
enum nnp_status {
	/** The call succeeded, and all output arguments now contain valid data. */
	nnp_status_success = 0,
	/** NNPACK function was called with batch_size == 0. */
	nnp_status_invalid_batch_size = 2,
	/** NNPACK function was called with channels == 0. */
	nnp_status_invalid_channels = 3,
	/** NNPACK function was called with input_channels == 0. */
	nnp_status_invalid_input_channels = 4,
	/** NNPACK function was called with output_channels == 0. */
	nnp_status_invalid_output_channels = 5,
	/** NNPACK function was called with input_size.height == 0 or input_size.width == 0 */
	nnp_status_invalid_input_size = 10,
	/** NNPACK function was called with input_stride.height == 0 or input_stride.width == 0 */
	nnp_status_invalid_input_stride = 11,
	/** NNPACK function was called with input_padding not less than respective kernel (or pooling) size, i.e.:
	 *
	 *  - input_padding.left   >= kernel_size.width  (>= pooling_size.width)
	 *  - input_padding.right  >= kernel_size.width  (>= pooling_size.width)
	 *  - input_padding.top    >= kernel_size.height (>= pooling_size.height)
	 *  - input_padding.bottom >= kernel_size.height (>= pooling_size.height)
	 */
	nnp_status_invalid_input_padding = 12,
	/** NNPACK function was called with kernel_size.height == 0 or kernel_size.width == 0 */
	nnp_status_invalid_kernel_size = 13,
	/** NNPACK function was called with pooling_size.height == 0 or pooling_size.width == 0 */
	nnp_status_invalid_pooling_size = 14,
	/** NNPACK function was called with pooling_stride.height == 0 or pooling_stride.width == 0 */
	nnp_status_invalid_pooling_stride = 15,
	/** NNPACK function was called with convolution algorithm not in nnp_convolution_algorithm enumeration */
	nnp_status_invalid_algorithm = 16,
	/** NNPACK function was called with convolution transform strategy not in nnp_convolution_transform_strategy enum */
	nnp_status_invalid_transform_strategy = 17,
	/** NNPACK function was called with output_subsampling.height == 0 or output_subsampling.width == 0 */
	nnp_status_invalid_output_subsampling = 13,
	/** NNPACK function was called with activation not in nnp_activation enum */
	nnp_status_invalid_activation = 14,
	/** NNPACK function was called with invalid activation parameters */
	nnp_status_invalid_activation_parameters = 15,

	/** NNPACK does not support the particular input size for the function */
	nnp_status_unsupported_input_size = 20,
	/** NNPACK does not support the particular input stride for the function */
	nnp_status_unsupported_input_stride = 21,
	/** NNPACK does not support the particular input padding for the function */
	nnp_status_unsupported_input_padding = 22,
	/** NNPACK does not support the particular kernel size for the function */
	nnp_status_unsupported_kernel_size = 23,
	/** NNPACK does not support the particular pooling size for the function */
	nnp_status_unsupported_pooling_size = 24,
	/** NNPACK does not support the particular pooling stride for the function */
	nnp_status_unsupported_pooling_stride = 25,
	/** NNPACK does not support the particular convolution algorithm for the function */
	nnp_status_unsupported_algorithm = 26,
	/** NNPACK does not support the particular convolution transform strategy for the algorithm */
	nnp_status_unsupported_transform_strategy = 27,
	/** NNPACK does not support the particular activation function for the function */
	nnp_status_unsupported_activation = 28,
	/** NNPACK does not support the particular activation function parameters for the function */
	nnp_status_unsupported_activation_parameters = 29, 

	/** NNPACK function was called before the library was initialized */
	nnp_status_uninitialized = 50,
	/** NNPACK does not implement this function for the host CPU */
	nnp_status_unsupported_hardware = 51,
	/** NNPACK failed to allocate memory for temporary buffers */
	nnp_status_out_of_memory = 52,
	/** Scratch space buffer is too small */
	nnp_status_insufficient_buffer = 53,
	/** Scratch space buffer is not properly aligned */
	nnp_status_misaligned_buffer = 54
};

/**
 * @brief Activation applied applied after a convolutional or fully-connected layer.
 */
enum nnp_activation {
	/** Identity activation f(x) := x, i.e. no transformation */
	nnp_activation_identity = 0,
	/** ReLU activation f(x) := max(0, x) */
	nnp_activation_relu = 1,
};

/**
 * @brief Algorithm for computing convolutional layers.
 */
enum nnp_convolution_algorithm {
	/** Let NNPACK choose the algorithm depending on layer parameters */
	nnp_convolution_algorithm_auto = 0,
	/** Tiled convolution based on 2D Fourier transform with 8x8 blocks. Supports kernels up to 8x8. */
	nnp_convolution_algorithm_ft8x8 = 1,
	/** Tiled convolution based on 2D Fourier transform with 16x16 blocks. Supports kernels up to 16x16. */
	nnp_convolution_algorithm_ft16x16 = 2,
	/** Tiled convolution based on 2D Winograd transform F(3x3, 6x6) with 8x8 blocks. Supports only 3x3 kernels. */
	nnp_convolution_algorithm_wt8x8 = 3,
	/** Direct convolution via implicit GEMM. */
	nnp_convolution_algorithm_implicit_gemm = 4,
	/** Direct convolution implementation. */
	nnp_convolution_algorithm_direct = 5,
	/**
	 * Tiled convolution based on 2D Winograd transform F(3x3, 6x6) with 8x8 blocks in FP16.
	 * Supports only 3x3 kernels. Implemented only for new ARM processors (with NEON-HP),
	 * on non-supported processors falls back to nnp_convolution_algorithm_wt8x8.
	 */
	nnp_convolution_algorithm_wt8x8_fp16 = 6,
};

enum nnp_convolution_transform_strategy {
	nnp_convolution_transform_strategy_compute = 1,
	nnp_convolution_transform_strategy_precompute = 2,
	nnp_convolution_transform_strategy_reuse = 3
};

/* For backward compatibility */
#define nnp_convolution_transform_strategy_block_based nnp_convolution_transform_strategy_compute
#define nnp_convolution_transform_strategy_tuple_based nnp_convolution_transform_strategy_compute

/**
 * @brief Size of images, kernels, and pooling filters in NNPACK.
 */
struct nnp_size {
	/** Width (horizontal size) of an image, kernel, or pooling filter. */
	size_t width;
	/** Height (vertical size) of an image, kernel, or pooling filter. */
	size_t height;
};

/**
 * @brief Padding of images in NNPACK.
 */
struct nnp_padding {
	/** Padding above the image data */
	size_t top;
	/** Padding on the right of image data */
	size_t right;
	/** Padding below the image data */
	size_t bottom;
	/** Padding on the left of image data */
	size_t left;
};

/**
 * @brief Profiling information about time spent in different phases of a function call.
 */
struct nnp_profile {
	/** Time spent inside the function call, in seconds. */
	double total;
	/** Time spend on transformation of the input or input gradient tensor, in seconds. */
	double input_transform;
	/** Time spend on transformation of the kernel or kernel gradient tensor, in seconds. */
	double kernel_transform;
	/** Time spend on transformation of the output or output gradient tensor, in seconds. */
	double output_transform;
	/** Time spend on multiplication-accumulation of transformed coefficients, in seconds. */
	double block_multiplication;
};

enum nnp_status nnp_initialize(void);

enum nnp_status nnp_deinitialize(void);

/**
 * @brief Computes output of a 2D convolutional layer from input and kernel tensors.
 * @details This function targets training of convolutional neural networks and performs forward propagation.
 *          It is optimized for moderate minibatch sizes (64-128) and can be inefficient on a small minibatch.
 *          For minibatch size 1, use nnp_convolution_inference for optimal performance.
 * @param algorithm The type of algorithm to use for convolution. Possible values are:
 *
 *    - nnp_convolution_algorithm_auto    -- let the function choose the algorithm.
 *    - nnp_convolution_algorithm_ft8x8   -- tiled convolution based on 2D Fourier transform with 8x8 blocks.
 *                                           Supports kernels up to 8x8.
 *    - nnp_convolution_algorithm_ft16x16 -- tiled convolution based on 2D Fourier transform with 16x16 blocks.
 *                                           Supports kernels up to 16x16.
 *    - nnp_convolution_algorithm_wt8x8   -- tiled convolution based on 2D Winograd transform F(3x3, 6x6).
 *                                           Supports only 3x3 kernels.
 *
 * @param batch_size The number of images on the input and output of the convolutional layer.
 * @param input_channels The number of channels (AKA features, dimensions) in the input images.
 * @param output_channels The number of channels (AKA features, dimensions) in the output images.
 * @param input_size Size of input images, excluding implicit zero-padding.
 * @param input_padding Implicit zero-padding of input images.
 * @param kernel_size Kernel size.
 * @param[in]  input  A 4D tensor input[batch_size][input_channels][input_size.height][input_size.width].
 * @param[in]  kernel A 4D tensor kernel[output_channels][input_channels][kernel_size.height][kernel_size.width].
 * @param[in]  bias   A 1D array bias[output_channels].
 * @param[out] output A 4D tensor output[batch_size][output_channels][output_size.height][output_size.width] where
 *                        output_size.height = (input_padding.top + input_size.height + input_padding.bottom) -
 *                                             (kernel_size.height - 1)
 *                        output_size.width  = (input_padding.left + input_size.width + input_padding.right) -
 *                                             (kernel_size.width - 1)
 * @param threadpool A thread pool for parallelization of the computation.
 *                   If threadpool is NULL, the computation would run on the caller thread without parallelization.
 * @param[out] profile An optional pointer to profiling structure.
 *                     If provided, the structure would record time spent in different phases of the computation.
 */

enum nnp_status nnp_convolution_output(
	enum nnp_convolution_algorithm algorithm,
	size_t batch_size,
	size_t input_channels,
	size_t output_channels,
	struct nnp_size input_size,
	struct nnp_padding input_padding,
	struct nnp_size kernel_size,
	const float* input,
	const float* kernel,
	const float* bias,
	float* output,
	void* workspace_buffer,
	size_t* workspace_size,
	enum nnp_activation activation,
	const void* activation_parameters,
	pthreadpool_t threadpool,
	struct nnp_profile* profile);

/**
 * @brief Computes gradient of input of a 2D convolutional layer from gradient of output and kernel tensors.
 * @details This function targets training of convolutional neural networks and performs backward propagation.
 *          It is optimized for moderate minibatch sizes (64-128) and can be inefficient on a small minibatch.
 * @param algorithm The type of algorithm to use for convolution. Possible values are:
 *
 *    - nnp_convolution_algorithm_auto    -- let the function choose the algorithm.
 *    - nnp_convolution_algorithm_ft8x8   -- tiled convolution based on 2D Fourier transform with 8x8 blocks.
 *                                           Supports kernels up to 8x8.
 *    - nnp_convolution_algorithm_ft16x16 -- tiled convolution based on 2D Fourier transform with 16x16 blocks.
 *                                           Supports kernels up to 16x16.
 *    - nnp_convolution_algorithm_wt8x8   -- tiled convolution based on 2D Winograd transform F(3x3, 6x6).
 *                                           Supports only 3x3 kernels.
 *
 * @param batch_size The number of images (and their gradients) on the input and output of the convolutional layer.
 * @param input_channels The number of channels (AKA features, dimensions) in the input images (and gradients).
 * @param output_channels The number of channels (AKA features, dimensions) in the output images (and gradients).
 * @param input_size Size of input images and their gradients, excluding implicit zero-padding.
 * @param input_padding Implicit zero-padding of input images.
 * @param kernel_size Kernel size.
 * @param[in]  grad_output A 4D tensor grad_output[batch_size][output_channels][output_size.height][output_size.width]
 *                         where
 *                           output_size.height = (input_padding.top + input_size.height + input_padding.bottom) -
 *                                                (kernel_size.height - 1)
 *                           output_size.width  = (input_padding.left + input_size.width + input_padding.right) -
 *                                                (kernel_size.width - 1)
 * @param[in]  kernel      A 4D tensor kernel[output_channels][input_channels][kernel_size.height][kernel_size.width].
 * @param[out] grad_input  A 4D tensor grad_input[batch_size][input_channels][input_size.height][input_size.width].
 * @param threadpool A thread pool for parallelization of the computation.
 *                   If threadpool is NULL, the computation would run on the caller thread without parallelization.
 * @param[out] profile An optional pointer to profiling structure.
 *                     If provided, the structure would record time spent in different phases of the computation.
 */
enum nnp_status nnp_convolution_input_gradient(
	enum nnp_convolution_algorithm algorithm,
	size_t batch_size,
	size_t input_channels,
	size_t output_channels,
	struct nnp_size input_size,
	struct nnp_padding input_padding,
	struct nnp_size kernel_size,
	const float* grad_output,
	const float* kernel,
	float* grad_input,
	void* workspace_buffer,
	size_t* workspace_size,
	enum nnp_activation activation,
	const void* activation_parameters,
	pthreadpool_t threadpool,
	struct nnp_profile* profile);

/**
 * @brief Computes gradient of kernel of a 2D convolutional layer from gradient of output and input tensors.
 * @details This function targets training of convolutional neural networks and performs backward propagation.
 *          It is optimized for moderate minibatch sizes (64-128) and can be inefficient on a small minibatch.
 * @param algorithm The type of algorithm to use for convolution. Possible values are:
 *
 *    - nnp_convolution_algorithm_auto    -- let the function choose the algorithm.
 *    - nnp_convolution_algorithm_ft8x8   -- tiled convolution based on 2D Fourier transform with 8x8 blocks.
 *                                           Supports kernels up to 8x8.
 *    - nnp_convolution_algorithm_ft16x16 -- tiled convolution based on 2D Fourier transform with 16x16 blocks.
 *                                           Supports kernels up to 16x16.
 *
 * @param batch_size The number of images (and their gradients) on the input and output of the convolutional layer.
 * @param input_channels The number of channels (AKA features, dimensions) in the input images.
 * @param output_channels The number of channels (AKA features, dimensions) in the output images (and gradients).
 * @param input_size Size of input images and their gradients, excluding implicit zero-padding.
 * @param input_padding Implicit zero-padding of input images.
 * @param kernel_size Kernel size.
 * @param[in]  input       A 4D tensor input[batch_size][input_channels][input_size.height][input_size.width].
 * @param[in]  grad_output A 4D tensor grad_output[batch_size][output_channels][output_size.height][output_size.width]
 *                         where
 *                           output_size.height = (input_padding.top + input_size.height + input_padding.bottom) -
 *                                                (kernel_size.height - 1)
 *                           output_size.width  = (input_padding.left + input_size.width + input_padding.right) -
 *                                                (kernel_size.width - 1)
 * @param[out] grad_kernel A 4D tensor
 *                         grad_kernel[output_channels][input_channels][kernel_size.height][kernel_size.width].
 * @param threadpool A thread pool for parallelization of the computation.
 *                   If threadpool is NULL, the computation would run on the caller thread without parallelization.
 * @param[out] profile An optional pointer to profiling structure.
 *                     If provided, the structure would record time spent in different phases of the computation.
 */
enum nnp_status nnp_convolution_kernel_gradient(
	enum nnp_convolution_algorithm algorithm,
	size_t batch_size,
	size_t input_channels,
	size_t output_channels,
	struct nnp_size input_size,
	struct nnp_padding input_padding,
	struct nnp_size kernel_size,
	const float* input,
	const float* grad_output,
	float* grad_kernel,
	void* workspace_buffer,
	size_t* workspace_size,
	enum nnp_activation activation,
	const void* activation_parameters,
	pthreadpool_t threadpool,
	struct nnp_profile* profile);

/**
 * @brief Computes output of a 2D convolutional layer for a single input image and a kernel tensor.
 * @details This function targets prediction with convolutional neural networks and performs forward propagation.
 * @param algorithm The type of algorithm to use for convolution. Possible values are:
 *
 *    - nnp_convolution_algorithm_auto    -- let the function choose the algorithm.
 *    - nnp_convolution_algorithm_ft8x8   -- tiled convolution based on 2D Fourier transform with 8x8 blocks.
 *                                           Supports kernels up to 8x8.
 *    - nnp_convolution_algorithm_ft16x16 -- tiled convolution based on 2D Fourier transform with 16x16 blocks.
 *                                           Supports kernels up to 16x16.
 *    - nnp_convolution_algorithm_wt8x8   -- tiled convolution based on 2D Winograd transform F(3x3, 6x6).
 *                                           Supports only 3x3 kernels.
 *
 * @param transform_strategy A strategy that guides computation of kernel transforms coefficients.
 *                           Possible values are:
 *
 *    - nnp_convolution_transform_strategy_block_based -- do multiplication-accumulations on blocks of transformed
 *                                                        coefficients.
 *    - nnp_convolution_transform_strategy_tuple_based -- do multiplication-accumulations on tuples of transformed
 *                                                        coefficients.
 *
 * @param input_channels The number of channels (AKA features, dimensions) in the input image.
 * @param output_channels The number of channels (AKA features, dimensions) in the output image.
 * @param input_size Size of input image, excluding implicit zero-padding.
 * @param input_padding Implicit zero-padding of input image.
 * @param kernel_size Kernel size.
 * @param output_subsampling Subsample region for output, also known as convolution stride.
 * @param[in]  input  A 3D tensor input[input_channels][input_size.height][input_size.width].
 * @param[in]  kernel A 4D tensor kernel[output_channels][input_channels][kernel_size.height][kernel_size.width].
 * @param[in]  bias   A 1D array bias[output_channels].
 * @param[out] output A 3D tensor output[output_channels][output_size.height][output_size.width] where
 *                        output_size.height = (input_padding.top + input_size.height + input_padding.bottom) -
 *                                             (kernel_size.height - 1)
 *                        output_size.width  = (input_padding.left + input_size.width + input_padding.right) -
 *                                             (kernel_size.width - 1)
 * @param[in] workspace_buffer Buffer for scratch memory used during computation. Buffer must be aligned on 64 bytes.
 *                             If workspace_buffer is NULL and workspace_size is non-NULL, NNPACK would store the size
 *                             of required workspace memory at the workspace_size location, and exit without
 *                             computations.
 *                             If workspace_buffer is NULL and workspace_size is NULL, NNPACK would allocate memory
 *                             before and deallocate after this computation, potentially at significant runtime cost.
 * @param[in,out] workspace_size Pointer to the size of workspace buffer.
 *                               If workspace_buffer is NULL, NNPACK will write the size of required scratch memory to
 *                               the location specified by this pointer.
 *                               If workspace_buffer is non-NULL, NNPACK expects workspace_size to specify the size of
 *                               the buffer, in bytes.
 *                               If workspace_size is NULL, workspace_buffer must be NULL as well. In this case NNPACK
 *                               would allocate memory before and deallocate after this computation, potentially at
 *                               significant runtime cost.
 * @param threadpool A thread pool for parallelization of the computation.
 *                   If threadpool is NULL, the computation would run on the caller thread without parallelization.
 * @param[out] profile An optional pointer to profiling structure.
 *                     If provided, the structure would record time spent in different phases of the computation.
 */
enum nnp_status nnp_convolution_inference(
	enum nnp_convolution_algorithm algorithm,
	enum nnp_convolution_transform_strategy transform_strategy,
	size_t input_channels,
	size_t output_channels,
	struct nnp_size input_size,
	struct nnp_padding input_padding,
	struct nnp_size kernel_size,
	struct nnp_size output_subsampling,
	const float* input,
	const float* kernel,
	const float* bias,
	float* output,
	void* workspace_buffer,
	size_t* workspace_size,
	enum nnp_activation activation,
	const void* activation_parameters,
	pthreadpool_t threadpool,
	struct nnp_profile* profile);

/**
 * @brief Computes output of a fully connected layer from input and kernel matrices.
 * @details This function targets training of convolutional neural networks and performs forward propagation.
 *          It is optimized for moderate minibatch sizes (64-128) and can be inefficient on a small minibatch.
 *          For minibatch size 1, use nnp_fully_connected_inference for optimal performance.
 * @param batch_size The number of vectors on the input and output of the fully connected layer.
 * @param input_channels The number of channels (AKA features, dimensions) in the input matrix.
 * @param output_channels The number of channels (AKA features, dimensions) in the output matrix.
 * @param[in]  input  A 2D matrix input[batch_size][input_channels].
 * @param[in]  kernel A 2D matrix kernel[output_channels][input_channels].
 * @param[out] output A 2D matrix output[batch_size][output_channels].
 * @param threadpool A thread pool for parallelization of the computation.
 *                   If threadpool is NULL, the computation would run on the caller thread without parallelization.
 */
enum nnp_status nnp_fully_connected_output(
	size_t batch_size,
	size_t input_channels,
	size_t output_channels,
	const float input[],
	const float kernel[],
	float output[],
	pthreadpool_t threadpool,
	struct nnp_profile* profile);

/**
 * @brief Computes output of a fully connected layer for a single input vector and a kernel matrix.
 * @details This function targets prediction with convolutional neural networks and performs forward propagation.
 * @param input_channels The number of channels (AKA features, dimensions) in the input vector.
 * @param output_channels The number of channels (AKA features, dimensions) in the output vector.
 * @param[in]  input  A 1D array input[input_channels] of FP32 elements.
 * @param[in]  kernel A 2D matrix kernel[output_channels][input_channels] of FP32 elements.
 * @param[out] output A 1D array output[output_channels] of FP32 elements.
 * @param threadpool A thread pool for parallelization of the computation.
 *                   If threadpool is NULL, the computation would run on the caller thread without parallelization.
 */
enum nnp_status nnp_fully_connected_inference(
	size_t input_channels,
	size_t output_channels,
	const float* input,
	const float* kernel,
	float* output,
	pthreadpool_t threadpool);

/**
 * @brief Computes output of a fully connected layer for a single input vector and a kernel matrix.
 * @details This function targets prediction with convolutional neural networks and performs forward propagation.
 * @param input_channels The number of channels (AKA features, dimensions) in the input vector.
 * @param output_channels The number of channels (AKA features, dimensions) in the output vector.
 * @param[in]  input  A 1D array input[input_channels] of FP32 elements.
 * @param[in]  kernel A 2D matrix kernel[output_channels][input_channels] of FP16 (ARM alternative format) elements.
 * @param[out] output A 1D array output[output_channels] of FP32 elements.
 * @param threadpool A thread pool for parallelization of the computation.
 *                   If threadpool is NULL, the computation would run on the caller thread without parallelization.
 */
enum nnp_status nnp_fully_connected_inference_f16f32(
	size_t input_channels,
	size_t output_channels,
	const float* input,
	const void* kernel,
	float* output,
	pthreadpool_t threadpool);

/**
 * @brief Computes output of a max-pooling layer for an input tensor.
 * @details This function targets both prediction and training of convolutional neural networks and performs forward
 *          propagation. Is is optimized for both large and small minibatch sizes.
 * @param batch_size The number of images on the input and output of the max-pooling layer.
 * @param channels   The number of channels (AKA features, dimensions) in both input and output images.
 * @param input_size Size of input images, excluding implicit zero-padding.
 * @param input_padding Implicit padding of input images. The padding pixels are ignored by the pooling filter, but
 *                      affect the output size.
 * @param pooling_size   Size of the pooling filter. Only 2x2 filter are currently supported.
 * @param pooling_stride Stride of the pooling filter. Only 2x2 strides are currently supported.
 * @param[in]  input  A 4D tensor input[batch_size][channels][input_size.height][input_size.width].
 * @param[out] output A 4D tensor output[batch_size][channels][output_size.height][output_size.width] where
 *                    output_size.height = ceil(
 *                      (input_padding.top + input_size.height + input_padding.bottom - pooling_size.height) /
 *                        pooling_stride.height) + 1
 *                    output_size.width = ceil(
 *                      (input_padding.left + input_size.width + input_padding.right - pooling_size.width) /
 *                        pooling_stride.width) + 1
 * @param threadpool A thread pool for parallelization of the computation.
 *                   If threadpool is NULL, the computation would run on the caller thread without parallelization.
 */
enum nnp_status nnp_max_pooling_output(
	size_t batch_size,
	size_t channels,
	struct nnp_size input_size,
	struct nnp_padding input_padding,
	struct nnp_size pooling_size,
	struct nnp_size pooling_stride,
	const float input[],
	float output[],
	pthreadpool_t threadpool);

/**
 * @brief Computes output of a softmax layer for an input matrix.
 * @details This function targets both prediction and training of convolutional neural networks and performs forward
 *          propagation. Is is optimized for both large and small minibatch sizes.
 * @param batch_size The number of vectors on the input and output of the softmax layer.
 * @param channels   The number of channels (AKA features, dimensions) in both input and output vectors.
 * @param[in]  input  A 2D matrix input[batch_size][channels].
 * @param[out] output A 2D matrix output[batch_size][channels].
 * @param threadpool A thread pool for parallelization of the computation.
 *                   If threadpool is NULL, the computation would run on the caller thread without parallelization.
 */
enum nnp_status nnp_softmax_output(
    size_t batch_size,
    size_t channels,
    const float input[],
    float output[],
    pthreadpool_t threadpool);

/**
 * @brief Computes output of a rectified linear unit (ReLU) layer for an input matrix.
 * @details This function targets both prediction and training of convolutional neural networks and performs forward
 *          propagation. Is is optimized for both large and small minibatch sizes.
 * @param batch_size The number of vectors on the input and output of the ReLU layer.
 * @param channels   The number of channels (AKA features, dimensions) in both input and output matrices.
 * @param[in]  input  A 2D matrix input[batch_size][channels].
 * @param[out] output A 2D matrix output[batch_size][channels].
 * @param threadpool A thread pool for parallelization of the computation.
 *                   If threadpool is NULL, the computation would run on the caller thread without parallelization.
 */
enum nnp_status nnp_relu_output(
	size_t batch_size,
	size_t channels,
	const float input[],
	float output[],
	float negative_slope,
	pthreadpool_t threadpool);

/**
 * @brief Computes gradient of input of a rectified linear unit (ReLU) layer from gradient of output and input matrices.
 * @details This function targets training of convolutional neural networks and performs backward propagation.
 *          Is is optimized for both large and small minibatch sizes.
 * @param batch_size The number of vectors on the input and output of the ReLU layer.
 * @param channels   The number of channels (AKA features, dimensions) in both input and output matrices.
 * @param[in]  input  A 2D matrix input[batch_size][channels].
 * @param[out] output A 2D matrix output[batch_size][channels].
 * @param threadpool A thread pool for parallelization of the computation.
 *                   If threadpool is NULL, the computation would run on the caller thread without parallelization.
 */
enum nnp_status nnp_relu_input_gradient(
	size_t batch_size,
	size_t channels,
	const float grad_output[],
	const float input[],
	float grad_input[],
	float negative_slope,
	pthreadpool_t threadpool);

#ifdef __cplusplus
} /* extern "C" */
#endif

#ifdef __cplusplus
// Backward compatible implementations for nnp_convolution_*, if we are in C++
// mode.
inline enum nnp_status nnp_convolution_output(
	enum nnp_convolution_algorithm algorithm,
	size_t batch_size,
	size_t input_channels,
	size_t output_channels,
	struct nnp_size input_size,
	struct nnp_padding input_padding,
	struct nnp_size kernel_size,
	const float input[],
	const float kernel[],
	const float bias[],
	float output[],
	pthreadpool_t threadpool,
	struct nnp_profile* profile)
{
	return nnp_convolution_output(
		algorithm,
		batch_size, input_channels, output_channels,
		input_size, input_padding, kernel_size,
		input, kernel, bias, output,
		NULL, NULL,
		nnp_activation_identity, NULL, threadpool, profile);
}

inline enum nnp_status nnp_convolution_input_gradient(
	enum nnp_convolution_algorithm algorithm,
	size_t batch_size,
	size_t input_channels,
	size_t output_channels,
	struct nnp_size input_size,
	struct nnp_padding input_padding,
	struct nnp_size kernel_size,
	const float grad_output[],
	const float kernel[],
	float grad_input[],
	pthreadpool_t threadpool,
	struct nnp_profile* profile)
{
	return nnp_convolution_input_gradient(
		algorithm,
		batch_size, input_channels, output_channels,
		input_size, input_padding, kernel_size,
		grad_output, kernel, grad_input,
		NULL, NULL,
		nnp_activation_identity, NULL, threadpool, profile);
}

inline enum nnp_status nnp_convolution_kernel_gradient(
	enum nnp_convolution_algorithm algorithm,
	size_t batch_size,
	size_t input_channels,
	size_t output_channels,
	struct nnp_size input_size,
	struct nnp_padding input_padding,
	struct nnp_size kernel_size,
	const float input[],
	const float grad_output[],
	float grad_kernel[],
	pthreadpool_t threadpool,
	struct nnp_profile* profile)
{
	return nnp_convolution_kernel_gradient(
		algorithm,
		batch_size, input_channels, output_channels,
		input_size, input_padding, kernel_size,
		input, grad_output, grad_kernel,
		NULL, NULL,
		nnp_activation_identity, NULL, threadpool, profile);
}

inline enum nnp_status nnp_convolution_inference(
	enum nnp_convolution_algorithm algorithm,
	enum nnp_convolution_transform_strategy transform_strategy,
	size_t input_channels,
	size_t output_channels,
	struct nnp_size input_size,
	struct nnp_padding input_padding,
	struct nnp_size kernel_size,
	struct nnp_size output_subsampling,
	const float input[],
	const float kernel[],
	const float bias[],
	float output[],
	pthreadpool_t threadpool,
	struct nnp_profile* profile) {
	return nnp_convolution_inference(
		algorithm, transform_strategy,
		input_channels, output_channels,
		input_size, input_padding, kernel_size, output_subsampling,
		input, kernel, bias, output, NULL, NULL,
		nnp_activation_identity, NULL,
		threadpool, profile);
}

#endif // __cplusplus