/*
 * Copyright (c) 2016, 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.
 */

#include "arm_compute/runtime/NEON/NEFunctions.h"

#include "arm_compute/core/Types.h"
#include "utils/Utils.h"

#include <cstring>
#include <iostream>

using namespace arm_compute;

void main_neon_copy_objects(int argc, const char **argv)
{
    ARM_COMPUTE_UNUSED(argc);
    ARM_COMPUTE_UNUSED(argv);

    /** [Copy objects example] */
    constexpr unsigned int width  = 4;
    constexpr unsigned int height = 3;
    constexpr unsigned int batch  = 2;

    auto *src_data = new float[width * height * batch];
    auto *dst_data = new float[width * height * batch];

    // Fill src_data with dummy values:
    for(unsigned int b = 0; b < batch; b++)
    {
        for(unsigned int h = 0; h < height; h++)
        {
            for(unsigned int w = 0; w < width; w++)
            {
                src_data[b * (width * height) + h * width + w] = static_cast<float>(100 * b + 10 * h + w);
            }
        }
    }

    Tensor         input, output;
    NESoftmaxLayer softmax;

    // Initialize the tensors dimensions and type:
    const TensorShape shape(width, height, batch);
    input.allocator()->init(TensorInfo(shape, 1, DataType::F32));
    output.allocator()->init(TensorInfo(shape, 1, DataType::F32));

    // Configure softmax:
    softmax.configure(&input, &output);

    // Allocate the input / output tensors:
    input.allocator()->allocate();
    output.allocator()->allocate();

    // Fill the input tensor:
    // Simplest way: create an iterator to iterate through each element of the input tensor:
    Window input_window;
    input_window.use_tensor_dimensions(input.info()->tensor_shape());
    std::cout << " Dimensions of the input's iterator:\n";
    std::cout << " X = [start=" << input_window.x().start() << ", end=" << input_window.x().end() << ", step=" << input_window.x().step() << "]\n";
    std::cout << " Y = [start=" << input_window.y().start() << ", end=" << input_window.y().end() << ", step=" << input_window.y().step() << "]\n";
    std::cout << " Z = [start=" << input_window.z().start() << ", end=" << input_window.z().end() << ", step=" << input_window.z().step() << "]\n";

    // Create an iterator:
    Iterator input_it(&input, input_window);

    // Iterate through the elements of src_data and copy them one by one to the input tensor:
    // This is equivalent to:
    // for( unsigned int z = 0; z < batch; ++z)
    // {
    //   for( unsigned int y = 0; y < height; ++y)
    //   {
    //     for( unsigned int x = 0; x < width; ++x)
    //     {
    //       *reinterpret_cast<float*>( input.buffer() + input.info()->offset_element_in_bytes(Coordinates(x,y,z))) = src_data[ z * (width*height) + y * width + x];
    //     }
    //   }
    // }
    // Except it works for an arbitrary number of dimensions
    execute_window_loop(input_window, [&](const Coordinates & id)
    {
        std::cout << "Setting item [" << id.x() << "," << id.y() << "," << id.z() << "]\n";
        *reinterpret_cast<float *>(input_it.ptr()) = src_data[id.z() * (width * height) + id.y() * width + id.x()];
    },
    input_it);

    // Run NEON softmax:
    softmax.run();

    // More efficient way: create an iterator to iterate through each row (instead of each element) of the output tensor:
    Window output_window;
    output_window.use_tensor_dimensions(output.info()->tensor_shape(), /* first_dimension =*/Window::DimY); // Iterate through the rows (not each element)
    std::cout << " Dimensions of the output's iterator:\n";
    std::cout << " X = [start=" << output_window.x().start() << ", end=" << output_window.x().end() << ", step=" << output_window.x().step() << "]\n";
    std::cout << " Y = [start=" << output_window.y().start() << ", end=" << output_window.y().end() << ", step=" << output_window.y().step() << "]\n";
    std::cout << " Z = [start=" << output_window.z().start() << ", end=" << output_window.z().end() << ", step=" << output_window.z().step() << "]\n";

    // Create an iterator:
    Iterator output_it(&output, output_window);

    // Iterate through the rows of the output tensor and copy them to dst_data:
    // This is equivalent to:
    // for( unsigned int z = 0; z < batch; ++z)
    // {
    //   for( unsigned int y = 0; y < height; ++y)
    //   {
    //     memcpy( dst_data + z * (width*height) + y * width, input.buffer() + input.info()->offset_element_in_bytes(Coordinates(0,y,z)), width * sizeof(float));
    //   }
    // }
    // Except it works for an arbitrary number of dimensions
    execute_window_loop(output_window, [&](const Coordinates & id)
    {
        std::cout << "Copying one row starting from [" << id.x() << "," << id.y() << "," << id.z() << "]\n";
        // Copy one whole row:
        memcpy(dst_data + id.z() * (width * height) + id.y() * width, output_it.ptr(), width * sizeof(float));
    },
    output_it);

    delete[] src_data;
    delete[] dst_data;
    /** [Copy objects example] */
}

/** Main program for the copy objects test
 *
 * @param[in] argc Number of arguments
 * @param[in] argv Arguments
 */
int main(int argc, const char **argv)
{
    return utils::run_example(argc, argv, main_neon_copy_objects);
}