#include <algorithm>
#include <cstdint>
#include <cstring>
#include <memory>
#include <stdexcept>

#include <nanobind/nanobind.h>
#include <nanobind/ndarray.h>
#include <nanobind/stl/array.h>
#include <nanobind/stl/string.h>
#include <nanobind/stl/vector.h>

#include "interface.hpp"

namespace nb = nanobind;
using namespace nb::literals;

namespace
{
using PoseArray2D =
    nb::ndarray<nb::numpy, const float, nb::shape<-1, -1, -1, 3>, nb::c_contig>;
using CountArray = nb::ndarray<nb::numpy, const uint32_t, nb::shape<-1>, nb::c_contig>;
using RoomArray = nb::ndarray<nb::numpy, const float, nb::shape<2, 3>, nb::c_contig>;
using PoseArray3D = nb::ndarray<nb::numpy, float, nb::shape<-1, -1, 4>, nb::c_contig>;

PoseBatch2D pose_batch_from_numpy(const PoseArray2D &poses_2d, const CountArray &person_counts)
{
    if (poses_2d.shape(0) != person_counts.shape(0))
    {
        throw std::invalid_argument("poses_2d and person_counts must have the same number of views.");
    }

    PoseBatch2D batch;
    batch.num_views = static_cast<size_t>(poses_2d.shape(0));
    batch.max_persons = static_cast<size_t>(poses_2d.shape(1));
    batch.num_joints = static_cast<size_t>(poses_2d.shape(2));
    batch.person_counts.assign(person_counts.data(), person_counts.data() + batch.num_views);

    for (size_t i = 0; i < batch.person_counts.size(); ++i)
    {
        if (batch.person_counts[i] > batch.max_persons)
        {
            throw std::invalid_argument("person_counts entries must not exceed the padded person dimension.");
        }
    }

    const size_t total_size = batch.num_views * batch.max_persons * batch.num_joints * 3;
    batch.data.resize(total_size);
    std::memcpy(batch.data.data(), poses_2d.data(), total_size * sizeof(float));
    return batch;
}

std::array<std::array<float, 3>, 2> roomparams_from_numpy(const RoomArray &roomparams)
{
    std::array<std::array<float, 3>, 2> result {};
    for (size_t i = 0; i < 2; ++i)
    {
        for (size_t j = 0; j < 3; ++j)
        {
            result[i][j] = roomparams(i, j);
        }
    }
    return result;
}

PoseArray3D pose_batch_to_numpy(PoseBatch3D batch)
{
    auto *storage = new std::vector<float>(std::move(batch.data));
    nb::capsule owner(storage, [](void *value) noexcept
    {
        delete static_cast<std::vector<float> *>(value);
    });

    const size_t shape[3] = {batch.num_persons, batch.num_joints, 4};
    return PoseArray3D(storage->data(), 3, shape, owner);
}
} // namespace

NB_MODULE(_core, m)
{
    nb::class_<Camera>(m, "Camera")
        .def(nb::init<>())
        .def_rw("name", &Camera::name)
        .def_rw("K", &Camera::K)
        .def_rw("DC", &Camera::DC)
        .def_rw("R", &Camera::R)
        .def_rw("T", &Camera::T)
        .def_rw("width", &Camera::width)
        .def_rw("height", &Camera::height)
        .def_rw("type", &Camera::type)
        .def("__repr__", [](const Camera &camera)
        {
            return camera.to_string();
        });

    nb::class_<Triangulator>(m, "Triangulator")
        .def(nb::init<float, size_t>(),
             "min_match_score"_a = 0.95f,
             "min_group_size"_a = 1)
        .def(
            "triangulate_poses",
            [](Triangulator &self,
               const PoseArray2D &poses_2d,
               const CountArray &person_counts,
               const std::vector<Camera> &cameras,
               const RoomArray &roomparams,
               const std::vector<std::string> &joint_names)
            {
                PoseBatch2D pose_batch = pose_batch_from_numpy(poses_2d, person_counts);
                auto room = roomparams_from_numpy(roomparams);
                PoseBatch3D poses_3d = self.triangulate_poses(pose_batch, cameras, room, joint_names);
                return pose_batch_to_numpy(std::move(poses_3d));
            },
            "poses_2d"_a,
            "person_counts"_a,
            "cameras"_a,
            "roomparams"_a,
            "joint_names"_a)
        .def("reset", &Triangulator::reset)
        .def("print_stats", &Triangulator::print_stats);
}