/*
 * Copyright (c) 2018-2020 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_CLFUSEBATCHNORMALIZATIONKERNEL_H
#define ARM_COMPUTE_CLFUSEBATCHNORMALIZATIONKERNEL_H

#include "src/core/CL/ICLKernel.h"

namespace arm_compute
{
// Forward declarations
class ICLTensor;

/** OpenCL kernel to fuse the batch normalization node to a preceding convolution node */
class CLFuseBatchNormalizationKernel : public ICLKernel
{
public:
    /** Default constructor */
    CLFuseBatchNormalizationKernel();
    /** Prevent instances of this class from being copied (As this class contains pointers) */
    CLFuseBatchNormalizationKernel(const CLFuseBatchNormalizationKernel &) = delete;
    /** Prevent instances of this class from being copied (As this class contains pointers) */
    CLFuseBatchNormalizationKernel &operator=(const CLFuseBatchNormalizationKernel &) = delete;
    /** Allow instances of this class to be moved */
    CLFuseBatchNormalizationKernel(CLFuseBatchNormalizationKernel &&) = default;
    /** Allow instances of this class to be moved */
    CLFuseBatchNormalizationKernel &operator=(CLFuseBatchNormalizationKernel &&) = default;
    /** Default destructor */
    ~CLFuseBatchNormalizationKernel() = default;
    /** Set the source, destination of the kernel
     *
     * @param[in]  input_weights Input weights tensor for convolution or depthwise convolution layer. Data type supported: F16/F32. Data layout supported: NCHW, NHWC
     * @param[in]  bn_mean       Batch normalization layer mean tensor. Same as @p input_weights
     * @param[in]  bn_var        Batch normalization layer variance tensor. Same as @p input_weights
     * @param[out] fused_weights Output fused weights tensor. It can be a nullptr in case of in-place computation. Same as @p input_weights
     * @param[out] fused_bias    Output fused bias tensor. It can be a nullptr in case of in-place computation and input_bias != nullptr. Same as @p input_weights
     * @param[in]  input_bias    (Optional) Input bias tensor for convolution or depthwise convolution layer. It can be a nullptr in case the bias tensor is not required. Same as @p input_weights
     * @param[in]  bn_beta       (Optional) Batch normalization layer beta tensor. It can be a nullptr in case the beta tensor is not required. Same as @p input_weights
     *                           @note if nullptr, bn_beta is set to 0.0
     * @param[in]  bn_gamma      (Optional) Batch normalization layer gamma tensor. It can be a nullptr in case the gamma tensor is not required. Same as @p input_weights
     *                           @note if nullptr, bn_gamma is set to 1.0
     * @param[in]  epsilon       (Optional) Batch normalization layer epsilon parameter. Defaults to 0.001f.
     * @param[in]  fbn_type      (Optional) Fused batch normalization type. Defaults to CONVOLUTION.
     */
    void configure(const ICLTensor           *input_weights,
                   const ICLTensor           *bn_mean,
                   const ICLTensor           *bn_var,
                   ICLTensor                 *fused_weights,
                   ICLTensor                 *fused_bias,
                   const ICLTensor           *input_bias = nullptr,
                   const ICLTensor           *bn_beta    = nullptr,
                   const ICLTensor           *bn_gamma   = nullptr,
                   float                      epsilon    = 0.001f,
                   FuseBatchNormalizationType fbn_type   = FuseBatchNormalizationType::CONVOLUTION);
    /** Set the source, destination of the kernel
     *
     * @param[in]  compile_context The compile context to be used.
     * @param[in]  input_weights   Input weights tensor for convolution or depthwise convolution layer. Data type supported: F16/F32. Data layout supported: NCHW, NHWC
     * @param[in]  bn_mean         Batch normalization layer mean tensor. Same as @p input_weights
     * @param[in]  bn_var          Batch normalization layer variance tensor. Same as @p input_weights
     * @param[out] fused_weights   Output fused weights tensor. It can be a nullptr in case of in-place computation. Same as @p input_weights
     * @param[out] fused_bias      Output fused bias tensor. It can be a nullptr in case of in-place computation and input_bias != nullptr. Same as @p input_weights
     * @param[in]  input_bias      (Optional) Input bias tensor for convolution or depthwise convolution layer. It can be a nullptr in case the bias tensor is not required. Same as @p input_weights
     * @param[in]  bn_beta         (Optional) Batch normalization layer beta tensor. It can be a nullptr in case the beta tensor is not required. Same as @p input_weights
     *                             @note if nullptr, bn_beta is set to 0.0
     * @param[in]  bn_gamma        (Optional) Batch normalization layer gamma tensor. It can be a nullptr in case the gamma tensor is not required. Same as @p input_weights
     *                             @note if nullptr, bn_gamma is set to 1.0
     * @param[in]  epsilon         (Optional) Batch normalization layer epsilon parameter. Defaults to 0.001f.
     * @param[in]  fbn_type        (Optional) Fused batch normalization type. Defaults to CONVOLUTION.
     */
    void configure(const CLCompileContext    &compile_context,
                   const ICLTensor           *input_weights,
                   const ICLTensor           *bn_mean,
                   const ICLTensor           *bn_var,
                   ICLTensor                 *fused_weights,
                   ICLTensor                 *fused_bias,
                   const ICLTensor           *input_bias = nullptr,
                   const ICLTensor           *bn_beta    = nullptr,
                   const ICLTensor           *bn_gamma   = nullptr,
                   float                      epsilon    = 0.001f,
                   FuseBatchNormalizationType fbn_type   = FuseBatchNormalizationType::CONVOLUTION);
    /** Static function to check if given info will lead to a valid configuration of @ref CLFuseBatchNormalizationKernel
     *
     * @param[in] input_weights Input weights tensor info for convolution or depthwise convolution layer. Data type supported: F16/F32. Data layout supported: NCHW, NHWC
     * @param[in] bn_mean       Batch normalization layer mean tensor info. Same as @p input_weights
     * @param[in] bn_var        Batch normalization layer variance tensor info. Same as @p input_weights
     * @param[in] fused_weights Output fused weights tensor info. It can be a nullptr in case of in-place computation. Same as @p input_weights
     * @param[in] fused_bias    Output fused bias tensor info. It can be a nullptr in case of in-place computation and input_bias != nullptr. Same as @p input_weights
     * @param[in] input_bias    (Optional) Input bias tensor info for convolution or depthwise convolution layer. It can be a nullptr in case the bias tensor is not required. Same as @p input_weights
     * @param[in] bn_beta       (Optional) Batch normalization layer beta tensor info. It can be a nullptr in case the beta tensor is not required. Same as @p input_weights
     *                          @note if nullptr, bn_beta is set to 0.0
     * @param[in] bn_gamma      (Optional) Batch normalization layer gamma tensor info. It can be a nullptr in case the gamma tensor is not required. Same as @p input_weights
     *                          @note if nullptr, bn_gamma is set to 1.0
     * @param[in] epsilon       (Optional) Batch normalization layer epsilon parameter. Defaults to 0.001f.
     * @param[in] fbn_type      (Optional) Fused batch normalization type. Defaults to CONVOLUTION.
     *
     * @return a status
     */
    static Status validate(const ITensorInfo         *input_weights,
                           const ITensorInfo         *bn_mean,
                           const ITensorInfo         *bn_var,
                           const ITensorInfo         *fused_weights,
                           const ITensorInfo         *fused_bias,
                           const ITensorInfo         *input_bias = nullptr,
                           const ITensorInfo         *bn_beta    = nullptr,
                           const ITensorInfo         *bn_gamma   = nullptr,
                           float                      epsilon    = 0.001f,
                           FuseBatchNormalizationType fbn_type   = FuseBatchNormalizationType::CONVOLUTION);

    // Inherited methods overridden:
    void run(const Window &window, cl::CommandQueue &queue) override;

private:
    const ICLTensor *_input_weights;
    const ICLTensor *_input_bias;
    const ICLTensor *_bn_mean;
    const ICLTensor *_bn_var;
    const ICLTensor *_bn_gamma;
    const ICLTensor *_bn_beta;
    ICLTensor       *_fused_weights;
    ICLTensor       *_fused_bias;
    float            _epsilon;
    bool             _run_in_place_weights;
    bool             _run_in_place_bias;
};
} // namespace arm_compute
#endif /*ARM_COMPUTE_CLFUSEBATCHNORMALIZATIONKERNEL_H */