#include <migraphx/shape.hpp>
#include <migraphx/argument.hpp>
#include <migraphx/gpu/device/nary.hpp>
#include <migraphx/gpu/device/pad.hpp>
#include <migraphx/gpu/device/tensor.hpp>
#include <migraphx/gpu/device/launch.hpp>
#include <migraphx/float_equal.hpp>

namespace migraphx {
inline namespace MIGRAPHX_INLINE_NS {
namespace gpu {
namespace device {

argument
pad(hipStream_t stream, argument result, argument arg1, float value, std::vector<std::int64_t> pads)
{
    std::size_t nelements = arg1.get_shape().elements();
    visit_all(result)([&](auto output) {
        auto* outptr                 = device_cast(output.data());
        using type                   = typename decltype(output)::value_type;
        device_type<type> device_val = value;
        if(float_equal(value, std::numeric_limits<float>::lowest()))
        {
            device_val = device_cast(std::numeric_limits<type>::lowest());
        }
        gs_launch(stream, result.get_shape().elements())([=](auto i) { outptr[i] = device_val; });
    });

    visit_all(result, arg1)([&](auto output, auto input) {
        visit_tensor_size(result.get_shape().lens().size(), [&](auto ndim) {
            std::size_t offsets[ndim];
            std::copy(pads.begin(), pads.begin() + ndim, offsets);
            auto* outptr      = output.data();
            const auto* inptr = input.data();
            hip_tensor_descriptor<ndim> desc_input(input.get_shape());
            hip_tensor_descriptor<ndim> desc_output(output.get_shape());
            gs_launch(stream, nelements)([=](auto i) {
                auto idx = desc_input.multi(i);
                for(std::size_t j = 0; j < ndim; j++)
                {
                    idx[j] += offsets[j];
                }
                outptr[desc_output.linear(idx)] = inptr[i];
            });
        });
    });
    return result;
}

} // namespace device
} // namespace gpu
} // namespace MIGRAPHX_INLINE_NS
} // namespace migraphx