/*
 * Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved.
 *
 * 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.
 */

#pragma once

#include <algorithm>
#include <array>
#include <functional>
#include <memory>
#include <sstream>
#include <iostream>
#include <utility>

#include "cudnn_frontend_Tensor.h" 
#include "cudnn_frontend_ConvDesc.h" 

namespace cudnn_frontend {

static cudnnStatus_t 
cudnnReorderFilterAndBiasInt8x32(cudnnHandle_t handle, 
        const Tensor_v8 &tensor, 
        const ConvDesc_v8 &conv_desc,
        void *dev_filter_ptr, void *reordered_filter_ptr,
        void *dev_bias_ptr,   void *reordered_bias_ptr) {

    auto cudnn_status = CUDNN_STATUS_SUCCESS;

    if (dev_filter_ptr && reordered_filter_ptr == nullptr) {
        return CUDNN_STATUS_BAD_PARAM;
    }
    if (dev_bias_ptr && reordered_bias_ptr == nullptr) {
        return CUDNN_STATUS_BAD_PARAM;
    }

    cudnnFilterDescriptor_t filterDesc = nullptr;

    cudnn_status = cudnnCreateFilterDescriptor(&filterDesc);
    if (cudnn_status != CUDNN_STATUS_SUCCESS) {return cudnn_status;}

    int conv_dims        = conv_desc.getDimensionCount();
    int tensor_dims      = tensor.getDimensionCount();
    auto non_shape_dims  = tensor_dims - conv_dims;
    
    if (non_shape_dims != 2 && non_shape_dims != 3) {
        return CUDNN_STATUS_BAD_PARAM;
    }

    if (conv_dims != 2 && conv_dims != 3) {
        return CUDNN_STATUS_BAD_PARAM;
    }

    int filter_dims_[5] = {1,1,1,1,1};
    int64_t const * filter_dims = tensor.getDimArray();
    filter_dims_[0] = static_cast<int> (filter_dims[0]); // n
    filter_dims_[1] = static_cast<int> ((non_shape_dims == 2) ? filter_dims[1] : filter_dims[2]) * 32; // c
    filter_dims_[2] = static_cast<int> ((non_shape_dims == 2) ? filter_dims[2] : filter_dims[3]); // d/h
    filter_dims_[3] = static_cast<int> ((non_shape_dims == 2) ? filter_dims[3] : filter_dims[4]); // h/w
    if (conv_dims == 3) {
        filter_dims_[4] = static_cast<int> ((non_shape_dims == 2) ? filter_dims[4] : filter_dims[5]); // w
    }

    cudnn_status = cudnnSetFilterNdDescriptor(filterDesc, CUDNN_DATA_INT8x32, CUDNN_TENSOR_NCHW_VECT_C, conv_dims + 2, filter_dims_);

    if (cudnn_status != CUDNN_STATUS_SUCCESS) {return cudnn_status;}

    int reorderBias = (dev_bias_ptr != nullptr);

    cudnn_status = cudnnReorderFilterAndBias(handle,
        filterDesc, CUDNN_DEFAULT_REORDER, dev_filter_ptr, reordered_filter_ptr, reorderBias, dev_bias_ptr, reordered_bias_ptr);

    cudnnDestroyFilterDescriptor(filterDesc);
    return cudnn_status;
}
}
