/*
 * Copyright (c) 2017 ARM Limited.
 *
 * SPDX-License-Identifier: MIT
 *
 * 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.
 */
#ifndef __ARM_COMPUTE_TEST_REFERENCE_REFERENCE_H__
#define __ARM_COMPUTE_TEST_REFERENCE_REFERENCE_H__

#include "arm_compute/runtime/Array.h"
#include "tests/RawTensor.h"
#include "tests/Types.h"

#include <map>
#include <vector>

namespace arm_compute
{
namespace test
{
namespace validation
{
/** Interface for reference implementations. */
class Reference
{
public:
    /** Compute reference Harris corners.
     *
     * @param[in] shape                 Shape of input tensor
     * @param[in] threshold             Minimum threshold with which to eliminate Harris Corner scores (computed using the normalized Sobel kernel).
     * @param[in] min_dist              Radial Euclidean distance for the euclidean distance stage
     * @param[in] sensitivity           Sensitivity threshold k from the Harris-Stephens equation
     * @param[in] gradient_size         The gradient window size to use on the input. The implementation supports 3, 5, and 7
     * @param[in] block_size            The block window size used to compute the Harris Corner score. The implementation supports 3, 5, and 7.
     * @param[in] border_mode           Border mode to use
     * @param[in] constant_border_value Constant value to use for borders if border_mode is set to CONSTANT.
     *
     * @return Computed corners' keypoints.
     */
    static KeyPointArray compute_reference_harris_corners(const TensorShape &shape, float threshold, float min_dist, float sensitivity,
                                                          int32_t gradient_size, int32_t block_size, BorderMode border_mode, uint8_t constant_border_value);
    /** Compute reference absolute difference.
     *
     * @param[in] shape  Shape of the input and output tensors.
     * @param[in] dt_in0 Data type of first input tensor.
     * @param[in] dt_in1 Data type of second input tensor.
     * @param[in] dt_out Data type of the output tensor.
     *
     * @return Computed raw tensor.
     */
    static RawTensor compute_reference_absolute_difference(const TensorShape &shape, DataType dt_in0, DataType dt_in1, DataType dt_out);
    /** Compute reference accumulate.
     *
     * @param[in] shape Shape of the input and output tensors.
     *
     * @return Computed raw tensor.
     */
    static RawTensor compute_reference_accumulate(const TensorShape &shape);
    /** Compute reference accumulate.
     *
     * @param[in] shape Shape of the input and output tensors.
     * @param[in] shift A uint32_t value within the range of [0, 15]
     *
     * @return Computed raw tensor.
     */
    static RawTensor compute_reference_accumulate_squared(const TensorShape &shape, uint32_t shift);
    /** Compute reference accumulate.
     *
     * @param[in] shape Shape of the input and output tensors.
     * @param[in] alpha A float value within the range of [0, 1]
     *
     * @return Computed raw tensor.
     */
    static RawTensor compute_reference_accumulate_weighted(const TensorShape &shape, float alpha);
    /** Compute reference pixel-wise multiplication
     *
     * @param[in] shape           Shape of the input and output tensors.
     * @param[in] dt_in0          Data type of first input tensor.
     * @param[in] dt_in1          Data type of second input tensor.
     * @param[in] dt_out          Data type of the output tensor.
     * @param[in] scale           Non-negative scale.
     * @param[in] convert_policy  Overflow policy of the operation.
     * @param[in] rounding_policy Rounding policy of the operation.
     *
     * @return Computed raw tensor.
     */
    static RawTensor compute_reference_pixel_wise_multiplication(const TensorShape &shape, DataType dt_in0, DataType dt_in1, DataType dt_out, float scale, ConvertPolicy convert_policy,
                                                                 RoundingPolicy rounding_policy);
    /** Compute reference pixel-wise multiplication.
     *
     * @param[in] shape                Shape of the input and output tensors.
     * @param[in] dt_in0               Data type of first input tensor.
     * @param[in] dt_in1               Data type of second input tensor.
     * @param[in] dt_out               Data type of the output tensor.
     * @param[in] scale                Scale to apply after multiplication. Must be positive.
     * @param[in] fixed_point_position Fixed point position that expresses the number of bits for the fractional part of the number.
     * @param[in] convert_policy       Overflow policy of the operation.
     * @param[in] rounding_policy      Rounding policy of the operation.
     *
     * @return Computed raw tensor.
     */
    static RawTensor compute_reference_fixed_point_pixel_wise_multiplication(const TensorShape &shape, DataType dt_in0, DataType dt_in1, DataType dt_out, float scale, int fixed_point_position,
                                                                             ConvertPolicy convert_policy, RoundingPolicy rounding_policy);
    /** Compute reference roi pooling layer.
     *
     * @param[in] shape     Shape of the input tensor.
     * @param[in] dt        Data type of input and output tensors.
     * @param[in] rois      Region of interest vector.
     * @param[in] pool_info ROI Pooling Layer information.
     */
    static RawTensor compute_reference_roi_pooling_layer(const TensorShape &shape, DataType dt, const std::vector<ROI> &rois, const ROIPoolingLayerInfo &pool_info);

protected:
    Reference()  = default;
    ~Reference() = default;
};
} // namespace validation
} // namespace test
} // namespace arm_compute
#endif /* __ARM_COMPUTE_TEST_REFERENCE_REFERENCE_H__ */