#include <algorithm>
#include <array>
#include <cstdint>
#include <stdexcept>
#include <vector>

#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 PoseArray3DConst =
    nb::ndarray<nb::numpy, const float, nb::shape<-1, -1, 4>, nb::c_contig>;
using PoseArray3D = nb::ndarray<nb::numpy, float, nb::shape<-1, -1, 4>, nb::c_contig>;
using PoseArray2DOut = nb::ndarray<nb::numpy, float, nb::shape<-1, 4>, nb::c_contig>;

PoseBatch2DView pose_batch_view_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.");
    }

    for (size_t i = 0; i < static_cast<size_t>(person_counts.shape(0)); ++i)
    {
        if (person_counts(i) > poses_2d.shape(1))
        {
            throw std::invalid_argument("person_counts entries must not exceed the padded person dimension.");
        }
    }

    return PoseBatch2DView {
        poses_2d.data(),
        person_counts.data(),
        static_cast<size_t>(poses_2d.shape(0)),
        static_cast<size_t>(poses_2d.shape(1)),
        static_cast<size_t>(poses_2d.shape(2)),
    };
}

PoseBatch3DView pose_batch3d_view_from_numpy(const PoseArray3DConst &poses_3d)
{
    return PoseBatch3DView {
        poses_3d.data(),
        static_cast<size_t>(poses_3d.shape(0)),
        static_cast<size_t>(poses_3d.shape(1)),
    };
}

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);
}

PoseArray3D pose_batch_to_numpy_copy(const PoseBatch3D &batch)
{
    PoseBatch3D copy = batch;
    return pose_batch_to_numpy(std::move(copy));
}

PoseArray2DOut pose_rows_to_numpy_copy(const std::vector<std::array<float, 4>> &rows)
{
    auto *storage = new std::vector<float>(rows.size() * 4, 0.0f);
    for (size_t row = 0; row < rows.size(); ++row)
    {
        for (size_t coord = 0; coord < 4; ++coord)
        {
            (*storage)[row * 4 + coord] = rows[row][coord];
        }
    }

    nb::capsule owner(storage, [](void *value) noexcept
    {
        delete static_cast<std::vector<float> *>(value);
    });

    const size_t shape[2] = {rows.size(), 4};
    return PoseArray2DOut(storage->data(), 2, shape, owner);
}

PoseArray3D merged_poses_to_numpy_copy(const std::vector<std::vector<std::array<float, 4>>> &poses)
{
    size_t num_poses = poses.size();
    size_t num_joints = 0;
    if (!poses.empty())
    {
        num_joints = poses[0].size();
    }

    auto *storage = new std::vector<float>(num_poses * num_joints * 4, 0.0f);
    for (size_t pose = 0; pose < num_poses; ++pose)
    {
        if (poses[pose].size() != num_joints)
        {
            delete storage;
            throw std::invalid_argument("Merged poses must use a consistent joint count.");
        }
        for (size_t joint = 0; joint < num_joints; ++joint)
        {
            for (size_t coord = 0; coord < 4; ++coord)
            {
                (*storage)[((pose * num_joints) + joint) * 4 + coord] = poses[pose][joint][coord];
            }
        }
    }

    nb::capsule owner(storage, [](void *value) noexcept
    {
        delete static_cast<std::vector<float> *>(value);
    });

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

NB_MODULE(_core, m)
{
    nb::enum_<CameraModel>(m, "CameraModel")
        .value("PINHOLE", CameraModel::Pinhole)
        .value("FISHEYE", CameraModel::Fisheye);

    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("model", &Camera::model)
        .def("__repr__", [](const Camera &camera)
        {
            return camera.to_string();
        });

    nb::class_<PairCandidate>(m, "PairCandidate")
        .def(nb::init<>())
        .def_rw("view1", &PairCandidate::view1)
        .def_rw("view2", &PairCandidate::view2)
        .def_rw("person1", &PairCandidate::person1)
        .def_rw("person2", &PairCandidate::person2)
        .def_rw("global_person1", &PairCandidate::global_person1)
        .def_rw("global_person2", &PairCandidate::global_person2);

    nb::class_<PreviousPoseMatch>(m, "PreviousPoseMatch")
        .def(nb::init<>())
        .def_rw("previous_pose_index", &PreviousPoseMatch::previous_pose_index)
        .def_rw("score_view1", &PreviousPoseMatch::score_view1)
        .def_rw("score_view2", &PreviousPoseMatch::score_view2)
        .def_rw("matched_view1", &PreviousPoseMatch::matched_view1)
        .def_rw("matched_view2", &PreviousPoseMatch::matched_view2)
        .def_rw("kept", &PreviousPoseMatch::kept)
        .def_rw("decision", &PreviousPoseMatch::decision);

    nb::class_<PreviousPoseFilterDebug>(m, "PreviousPoseFilterDebug")
        .def(nb::init<>())
        .def_rw("used_previous_poses", &PreviousPoseFilterDebug::used_previous_poses)
        .def_rw("matches", &PreviousPoseFilterDebug::matches)
        .def_rw("kept_pair_indices", &PreviousPoseFilterDebug::kept_pair_indices)
        .def_rw("kept_pairs", &PreviousPoseFilterDebug::kept_pairs);

    nb::class_<CoreProposalDebug>(m, "CoreProposalDebug")
        .def(nb::init<>())
        .def_rw("pair_index", &CoreProposalDebug::pair_index)
        .def_rw("pair", &CoreProposalDebug::pair)
        .def_rw("score", &CoreProposalDebug::score)
        .def_rw("kept", &CoreProposalDebug::kept)
        .def_rw("drop_reason", &CoreProposalDebug::drop_reason)
        .def_prop_ro("pose_3d", [](const CoreProposalDebug &proposal)
        {
            return pose_rows_to_numpy_copy(proposal.pose_3d);
        }, nb::rv_policy::move);

    nb::class_<ProposalGroupDebug>(m, "ProposalGroupDebug")
        .def(nb::init<>())
        .def_rw("center", &ProposalGroupDebug::center)
        .def_rw("proposal_indices", &ProposalGroupDebug::proposal_indices)
        .def_prop_ro("pose_3d", [](const ProposalGroupDebug &group)
        {
            return pose_rows_to_numpy_copy(group.pose_3d);
        }, nb::rv_policy::move);

    nb::class_<GroupingDebug>(m, "GroupingDebug")
        .def(nb::init<>())
        .def_rw("initial_groups", &GroupingDebug::initial_groups)
        .def_rw("duplicate_pair_drops", &GroupingDebug::duplicate_pair_drops)
        .def_rw("groups", &GroupingDebug::groups);

    nb::class_<FullProposalDebug>(m, "FullProposalDebug")
        .def(nb::init<>())
        .def_rw("source_core_proposal_index", &FullProposalDebug::source_core_proposal_index)
        .def_rw("pair", &FullProposalDebug::pair)
        .def_prop_ro("pose_3d", [](const FullProposalDebug &proposal)
        {
            return pose_rows_to_numpy_copy(proposal.pose_3d);
        }, nb::rv_policy::move);

    nb::class_<MergeDebug>(m, "MergeDebug")
        .def(nb::init<>())
        .def_rw("group_proposal_indices", &MergeDebug::group_proposal_indices)
        .def_prop_ro("merged_poses", [](const MergeDebug &merge)
        {
            return merged_poses_to_numpy_copy(merge.merged_poses);
        }, nb::rv_policy::move);

    nb::class_<TriangulationTrace>(m, "TriangulationTrace")
        .def(nb::init<>())
        .def_rw("pairs", &TriangulationTrace::pairs)
        .def_rw("previous_filter", &TriangulationTrace::previous_filter)
        .def_rw("core_proposals", &TriangulationTrace::core_proposals)
        .def_rw("grouping", &TriangulationTrace::grouping)
        .def_rw("full_proposals", &TriangulationTrace::full_proposals)
        .def_rw("merge", &TriangulationTrace::merge)
        .def_prop_ro("final_poses", [](const TriangulationTrace &trace)
        {
            return pose_batch_to_numpy_copy(trace.final_poses);
        }, nb::rv_policy::move);

    m.def(
        "build_pair_candidates",
        [](const PoseArray2D &poses_2d, const CountArray &person_counts)
        {
            return build_pair_candidates(pose_batch_view_from_numpy(poses_2d, person_counts));
        },
        "poses_2d"_a,
        "person_counts"_a);

    m.def(
        "filter_pairs_with_previous_poses",
        [](const PoseArray2D &poses_2d,
           const CountArray &person_counts,
           const std::vector<Camera> &cameras,
           const std::vector<std::string> &joint_names,
           const PoseArray3DConst &previous_poses_3d,
           float min_match_score)
        {
            return filter_pairs_with_previous_poses(
                pose_batch_view_from_numpy(poses_2d, person_counts),
                cameras,
                joint_names,
                pose_batch3d_view_from_numpy(previous_poses_3d),
                min_match_score);
        },
        "poses_2d"_a,
        "person_counts"_a,
        "cameras"_a,
        "joint_names"_a,
        "previous_poses_3d"_a,
        "min_match_score"_a = 0.95f);

    m.def(
        "triangulate_debug",
        [](const PoseArray2D &poses_2d,
           const CountArray &person_counts,
           const std::vector<Camera> &cameras,
           const RoomArray &roomparams,
           const std::vector<std::string> &joint_names,
           float min_match_score,
           size_t min_group_size)
        {
            return triangulate_debug(
                pose_batch_view_from_numpy(poses_2d, person_counts),
                cameras,
                roomparams_from_numpy(roomparams),
                joint_names,
                nullptr,
                min_match_score,
                min_group_size);
        },
        "poses_2d"_a,
        "person_counts"_a,
        "cameras"_a,
        "roomparams"_a,
        "joint_names"_a,
        "min_match_score"_a = 0.95f,
        "min_group_size"_a = 1);

    m.def(
        "triangulate_debug",
        [](const PoseArray2D &poses_2d,
           const CountArray &person_counts,
           const std::vector<Camera> &cameras,
           const RoomArray &roomparams,
           const std::vector<std::string> &joint_names,
           const PoseArray3DConst &previous_poses_3d,
           float min_match_score,
           size_t min_group_size)
        {
            const PoseBatch3DView previous_view = pose_batch3d_view_from_numpy(previous_poses_3d);
            return triangulate_debug(
                pose_batch_view_from_numpy(poses_2d, person_counts),
                cameras,
                roomparams_from_numpy(roomparams),
                joint_names,
                &previous_view,
                min_match_score,
                min_group_size);
        },
        "poses_2d"_a,
        "person_counts"_a,
        "cameras"_a,
        "roomparams"_a,
        "joint_names"_a,
        "previous_poses_3d"_a,
        "min_match_score"_a = 0.95f,
        "min_group_size"_a = 1);

    m.def(
        "triangulate_poses",
        [](const PoseArray2D &poses_2d,
           const CountArray &person_counts,
           const std::vector<Camera> &cameras,
           const RoomArray &roomparams,
           const std::vector<std::string> &joint_names,
           float min_match_score,
           size_t min_group_size)
        {
            const PoseBatch3D poses_3d = triangulate_poses(
                pose_batch_view_from_numpy(poses_2d, person_counts),
                cameras,
                roomparams_from_numpy(roomparams),
                joint_names,
                nullptr,
                min_match_score,
                min_group_size);
            return pose_batch_to_numpy(poses_3d);
        },
        "poses_2d"_a,
        "person_counts"_a,
        "cameras"_a,
        "roomparams"_a,
        "joint_names"_a,
        "min_match_score"_a = 0.95f,
        "min_group_size"_a = 1);

    m.def(
        "triangulate_poses",
        [](const PoseArray2D &poses_2d,
           const CountArray &person_counts,
           const std::vector<Camera> &cameras,
           const RoomArray &roomparams,
           const std::vector<std::string> &joint_names,
           const PoseArray3DConst &previous_poses_3d,
           float min_match_score,
           size_t min_group_size)
        {
            const PoseBatch3DView previous_view = pose_batch3d_view_from_numpy(previous_poses_3d);
            const PoseBatch3D poses_3d = triangulate_poses(
                pose_batch_view_from_numpy(poses_2d, person_counts),
                cameras,
                roomparams_from_numpy(roomparams),
                joint_names,
                &previous_view,
                min_match_score,
                min_group_size);
            return pose_batch_to_numpy(poses_3d);
        },
        "poses_2d"_a,
        "person_counts"_a,
        "cameras"_a,
        "roomparams"_a,
        "joint_names"_a,
        "previous_poses_3d"_a,
        "min_match_score"_a = 0.95f,
        "min_group_size"_a = 1);
}