text: /*
      pybind11/eigen.h: Transparent conversion for dense and sparse Eigen matrices
  
      Copyright (c) 2016 Wenzel Jakob <wenzel.jakob@epfl.ch>
  
      All rights reserved. Use of this source code is governed by a
      BSD-style license that can be found in the LICENSE file.
  */
  
  #pragma once
  
  #include "numpy.h"
  
  #if defined(__INTEL_COMPILER)
  #  pragma warning(disable: 1682) // implicit conversion of a 64-bit integral type to a smaller integral type (potential portability problem)
  #elif defined(__GNUG__) || defined(__clang__)
  #  pragma GCC diagnostic push
  #  pragma GCC diagnostic ignored "-Wconversion"
  #  pragma GCC diagnostic ignored "-Wdeprecated-declarations"
  #  ifdef __clang__
  //   Eigen generates a bunch of implicit-copy-constructor-is-deprecated warnings with -Wdeprecated
  //   under Clang, so disable that warning here:
  #    pragma GCC diagnostic ignored "-Wdeprecated"
  #  endif
  #  if __GNUC__ >= 7
  #    pragma GCC diagnostic ignored "-Wint-in-bool-context"
  #  endif
  #endif
  
  #if defined(_MSC_VER)
  #  pragma warning(push)
  #  pragma warning(disable: 4127) // warning C4127: Conditional expression is constant
  #  pragma warning(disable: 4996) // warning C4996: std::unary_negate is deprecated in C++17
  #endif
  
  #include <Eigen/Core>
  #include <Eigen/SparseCore>
  
  // Eigen prior to 3.2.7 doesn't have proper move constructors--but worse, some classes get implicit
  // move constructors that break things.  We could detect this an explicitly copy, but an extra copy
  // of matrices seems highly undesirable.
  static_assert(EIGEN_VERSION_AT_LEAST(3,2,7), "Eigen support in pybind11 requires Eigen >= 3.2.7");
  
  PYBIND11_NAMESPACE_BEGIN(PYBIND11_NAMESPACE)
  
  // Provide a convenience alias for easier pass-by-ref usage with fully dynamic strides:
  using EigenDStride = Eigen::Stride<Eigen::Dynamic, Eigen::Dynamic>;
  template <typename MatrixType> using EigenDRef = Eigen::Ref<MatrixType, 0, EigenDStride>;
  template <typename MatrixType> using EigenDMap = Eigen::Map<MatrixType, 0, EigenDStride>;
  
  PYBIND11_NAMESPACE_BEGIN(detail)
  
  #if EIGEN_VERSION_AT_LEAST(3,3,0)
  using EigenIndex = Eigen::Index;
  #else
  using EigenIndex = EIGEN_DEFAULT_DENSE_INDEX_TYPE;
  #endif
  
  // Matches Eigen::Map, Eigen::Ref, blocks, etc:
  template <typename T> using is_eigen_dense_map = all_of<is_template_base_of<Eigen::DenseBase, T>, std::is_base_of<Eigen::MapBase<T, Eigen::ReadOnlyAccessors>, T>>;
  template <typename T> using is_eigen_mutable_map = std::is_base_of<Eigen::MapBase<T, Eigen::WriteAccessors>, T>;
  template <typename T> using is_eigen_dense_plain = all_of<negation<is_eigen_dense_map<T>>, is_template_base_of<Eigen::PlainObjectBase, T>>;
  template <typename T> using is_eigen_sparse = is_template_base_of<Eigen::SparseMatrixBase, T>;
  // Test for objects inheriting from EigenBase<Derived> that aren't captured by the above.  This
  // basically covers anything that can be assigned to a dense matrix but that don't have a typical
  // matrix data layout that can be copied from their .data().  For example, DiagonalMatrix and
  // SelfAdjointView fall into this category.
  template <typename T> using is_eigen_other = all_of<
      is_template_base_of<Eigen::EigenBase, T>,
      negation<any_of<is_eigen_dense_map<T>, is_eigen_dense_plain<T>, is_eigen_sparse<T>>>
  >;
  
  // Captures numpy/eigen conformability status (returned by EigenProps::conformable()):
  template <bool EigenRowMajor> struct EigenConformable {
      bool conformable = false;
      EigenIndex rows = 0, cols = 0;
      EigenDStride stride{0, 0};      // Only valid if negativestrides is false!
      bool negativestrides = false;   // If true, do not use stride!
  
      EigenConformable(bool fits = false) : conformable{fits} {}
      // Matrix type:
      EigenConformable(EigenIndex r, EigenIndex c,
              EigenIndex rstride, EigenIndex cstride) :
          conformable{true}, rows{r}, cols{c} {
          // TODO: when Eigen bug #747 is fixed, remove the tests for non-negativity. http://eigen.tuxfamily.org/bz/show_bug.cgi?id=747
          if (rstride < 0 || cstride < 0) {
              negativestrides = true;
          } else {
              stride = {EigenRowMajor ? rstride : cstride /* outer stride */,
                        EigenRowMajor ? cstride : rstride /* inner stride */ };
          }
      }
      // Vector type:
      EigenConformable(EigenIndex r, EigenIndex c, EigenIndex stride)
          : EigenConformable(r, c, r == 1 ? c*stride : stride, c == 1 ? r : r*stride) {}
  
      template <typename props> bool stride_compatible() const {
          // To have compatible strides, we need (on both dimensions) one of fully dynamic strides,
          // matching strides, or a dimension size of 1 (in which case the stride value is irrelevant)
          return
              !negativestrides &&
              (props::inner_stride == Eigen::Dynamic || props::inner_stride == stride.inner() ||
                  (EigenRowMajor ? cols : rows) == 1) &&
              (props::outer_stride == Eigen::Dynamic || props::outer_stride == stride.outer() ||
                  (EigenRowMajor ? rows : cols) == 1);
      }
      operator bool() const { return conformable; }
  };
  
  template <typename Type> struct eigen_extract_stride { using type = Type; };
  template <typename PlainObjectType, int MapOptions, typename StrideType>
  struct eigen_extract_stride<Eigen::Map<PlainObjectType, MapOptions, StrideType>> { using type = StrideType; };
  template <typename PlainObjectType, int Options, typename StrideType>
  struct eigen_extract_stride<Eigen::Ref<PlainObjectType, Options, StrideType>> { using type = StrideType; };
  
  // Helper struct for extracting information from an Eigen type
  template <typename Type_> struct EigenProps {
      using Type = Type_;
      using Scalar = typename Type::Scalar;
      using StrideType = typename eigen_extract_stride<Type>::type;
      static constexpr EigenIndex
          rows = Type::RowsAtCompileTime,
          cols = Type::ColsAtCompileTime,
          size = Type::SizeAtCompileTime;
      static constexpr bool
          row_major = Type::IsRowMajor,
          vector = Type::IsVectorAtCompileTime, // At least one dimension has fixed size 1
          fixed_rows = rows != Eigen::Dynamic,
          fixed_cols = cols != Eigen::Dynamic,
          fixed = size != Eigen::Dynamic, // Fully-fixed size
          dynamic = !fixed_rows && !fixed_cols; // Fully-dynamic size
  
      template <EigenIndex i, EigenIndex ifzero> using if_zero = std::integral_constant<EigenIndex, i == 0 ? ifzero : i>;
      static constexpr EigenIndex inner_stride = if_zero<StrideType::InnerStrideAtCompileTime, 1>::value,
                                  outer_stride = if_zero<StrideType::OuterStrideAtCompileTime,
                                                         vector ? size : row_major ? cols : rows>::value;
      static constexpr bool dynamic_stride = inner_stride == Eigen::Dynamic && outer_stride == Eigen::Dynamic;
      static constexpr bool requires_row_major = !dynamic_stride && !vector && (row_major ? inner_stride : outer_stride) == 1;
      static constexpr bool requires_col_major = !dynamic_stride && !vector && (row_major ? outer_stride : inner_stride) == 1;
  
      // Takes an input array and determines whether we can make it fit into the Eigen type.  If
      // the array is a vector, we attempt to fit it into either an Eigen 1xN or Nx1 vector
      // (preferring the latter if it will fit in either, i.e. for a fully dynamic matrix type).
      static EigenConformable<row_major> conformable(const array &a) {
          const auto dims = a.ndim();
          if (dims < 1 || dims > 2)
              return false;
  
          if (dims == 2) { // Matrix type: require exact match (or dynamic)
  
              EigenIndex
                  np_rows = a.shape(0),
                  np_cols = a.shape(1),
                  np_rstride = a.strides(0) / static_cast<ssize_t>(sizeof(Scalar)),
                  np_cstride = a.strides(1) / static_cast<ssize_t>(sizeof(Scalar));
              if ((fixed_rows && np_rows != rows) || (fixed_cols && np_cols != cols))
                  return false;
  
              return {np_rows, np_cols, np_rstride, np_cstride};
          }
  
          // Otherwise we're storing an n-vector.  Only one of the strides will be used, but whichever
          // is used, we want the (single) numpy stride value.
          const EigenIndex n = a.shape(0),
                stride = a.strides(0) / static_cast<ssize_t>(sizeof(Scalar));
  
          if (vector) { // Eigen type is a compile-time vector
              if (fixed && size != n)
                  return false; // Vector size mismatch
              return {rows == 1 ? 1 : n, cols == 1 ? 1 : n, stride};
          }
          else if (fixed) {
              // The type has a fixed size, but is not a vector: abort
              return false;
          }
          else if (fixed_cols) {
              // Since this isn't a vector, cols must be != 1.  We allow this only if it exactly
              // equals the number of elements (rows is Dynamic, and so 1 row is allowed).
              if (cols != n) return false;
              return {1, n, stride};
          }
          else {
              // Otherwise it's either fully dynamic, or column dynamic; both become a column vector
              if (fixed_rows && rows != n) return false;
              return {n, 1, stride};
          }
      }
  
      static constexpr bool show_writeable = is_eigen_dense_map<Type>::value && is_eigen_mutable_map<Type>::value;
      static constexpr bool show_order = is_eigen_dense_map<Type>::value;
      static constexpr bool show_c_contiguous = show_order && requires_row_major;
      static constexpr bool show_f_contiguous = !show_c_contiguous && show_order && requires_col_major;
  
      static constexpr auto descriptor =
          _("numpy.ndarray[") + npy_format_descriptor<Scalar>::name +
          _("[")  + _<fixed_rows>(_<(size_t) rows>(), _("m")) +
          _(", ") + _<fixed_cols>(_<(size_t) cols>(), _("n")) +
          _("]") +
          // For a reference type (e.g. Ref<MatrixXd>) we have other constraints that might need to be
          // satisfied: writeable=True (for a mutable reference), and, depending on the map's stride
          // options, possibly f_contiguous or c_contiguous.  We include them in the descriptor output
          // to provide some hint as to why a TypeError is occurring (otherwise it can be confusing to
          // see that a function accepts a 'numpy.ndarray[float64[3,2]]' and an error message that you
          // *gave* a numpy.ndarray of the right type and dimensions.
          _<show_writeable>(", flags.writeable", "") +
          _<show_c_contiguous>(", flags.c_contiguous", "") +
          _<show_f_contiguous>(", flags.f_contiguous", "") +
          _("]");
  };
  
  // Casts an Eigen type to numpy array.  If given a base, the numpy array references the src data,
  // otherwise it'll make a copy.  writeable lets you turn off the writeable flag for the array.
  template <typename props> handle eigen_array_cast(typename props::Type const &src, handle base = handle(), bool writeable = true) {
      constexpr ssize_t elem_size = sizeof(typename props::Scalar);
      array a;
      if (props::vector)
          a = array({ src.size() }, { elem_size * src.innerStride() }, src.data(), base);
      else
          a = array({ src.rows(), src.cols() }, { elem_size * src.rowStride(), elem_size * src.colStride() },
                    src.data(), base);
  
      if (!writeable)
          array_proxy(a.ptr())->flags &= ~detail::npy_api::NPY_ARRAY_WRITEABLE_;
  
      return a.release();
  }
  
  // Takes an lvalue ref to some Eigen type and a (python) base object, creating a numpy array that
  // reference the Eigen object's data with `base` as the python-registered base class (if omitted,
  // the base will be set to None, and lifetime management is up to the caller).  The numpy array is
  // non-writeable if the given type is const.
  template <typename props, typename Type>
  handle eigen_ref_array(Type &src, handle parent = none()) {
      // none here is to get past array's should-we-copy detection, which currently always
      // copies when there is no base.  Setting the base to None should be harmless.
      return eigen_array_cast<props>(src, parent, !std::is_const<Type>::value);
  }
  
  // Takes a pointer to some dense, plain Eigen type, builds a capsule around it, then returns a numpy
  // array that references the encapsulated data with a python-side reference to the capsule to tie
  // its destruction to that of any dependent python objects.  Const-ness is determined by whether or
  // not the Type of the pointer given is const.
  template <typename props, typename Type, typename = enable_if_t<is_eigen_dense_plain<Type>::value>>
  handle eigen_encapsulate(Type *src) {
      capsule base(src, [](void *o) { delete static_cast<Type *>(o); });
      return eigen_ref_array<props>(*src, base);
  }
  
  // Type caster for regular, dense matrix types (e.g. MatrixXd), but not maps/refs/etc. of dense
  // types.
  template<typename Type>
  struct type_caster<Type, enable_if_t<is_eigen_dense_plain<Type>::value>> {
      using Scalar = typename Type::Scalar;
      using props = EigenProps<Type>;
  
      bool load(handle src, bool convert) {
          // If we're in no-convert mode, only load if given an array of the correct type
          if (!convert && !isinstance<array_t<Scalar>>(src))
              return false;
  
          // Coerce into an array, but don't do type conversion yet; the copy below handles it.
          auto buf = array::ensure(src);
  
          if (!buf)
              return false;
  
          auto dims = buf.ndim();
          if (dims < 1 || dims > 2)
              return false;
  
          auto fits = props::conformable(buf);
          if (!fits)
              return false;
  
          // Allocate the new type, then build a numpy reference into it
          value = Type(fits.rows, fits.cols);
          auto ref = reinterpret_steal<array>(eigen_ref_array<props>(value));
          if (dims == 1) ref = ref.squeeze();
          else if (ref.ndim() == 1) buf = buf.squeeze();
  
          int result = detail::npy_api::get().PyArray_CopyInto_(ref.ptr(), buf.ptr());
  
          if (result < 0) { // Copy failed!
              PyErr_Clear();
              return false;
          }
  
          return true;
      }
  
  private:
  
      // Cast implementation
      template <typename CType>
      static handle cast_impl(CType *src, return_value_policy policy, handle parent) {
          switch (policy) {
              case return_value_policy::take_ownership:
              case return_value_policy::automatic:
                  return eigen_encapsulate<props>(src);
              case return_value_policy::move:
                  return eigen_encapsulate<props>(new CType(std::move(*src)));
              case return_value_policy::copy:
                  return eigen_array_cast<props>(*src);
              case return_value_policy::reference:
              case return_value_policy::automatic_reference:
                  return eigen_ref_array<props>(*src);
              case return_value_policy::reference_internal:
                  return eigen_ref_array<props>(*src, parent);
              default:
                  throw cast_error("unhandled return_value_policy: should not happen!");
          };
      }
  
  public:
  
      // Normal returned non-reference, non-const value:
      static handle cast(Type &&src, return_value_policy /* policy */, handle parent) {
          return cast_impl(&src, return_value_policy::move, parent);
      }
      // If you return a non-reference const, we mark the numpy array readonly:
      static handle cast(const Type &&src, return_value_policy /* policy */, handle parent) {
          return cast_impl(&src, return_value_policy::move, parent);
      }
      // lvalue reference return; default (automatic) becomes copy
      static handle cast(Type &src, return_value_policy policy, handle parent) {
          if (policy == return_value_policy::automatic || policy == return_value_policy::automatic_reference)
              policy = return_value_policy::copy;
          return cast_impl(&src, policy, parent);
      }
      // const lvalue reference return; default (automatic) becomes copy
      static handle cast(const Type &src, return_value_policy policy, handle parent) {
          if (policy == return_value_policy::automatic || policy == return_value_policy::automatic_reference)
              policy = return_value_policy::copy;
          return cast(&src, policy, parent);
      }
      // non-const pointer return
      static handle cast(Type *src, return_value_policy policy, handle parent) {
          return cast_impl(src, policy, parent);
      }
      // const pointer return
      static handle cast(const Type *src, return_value_policy policy, handle parent) {
          return cast_impl(src, policy, parent);
      }
  
      static constexpr auto name = props::descriptor;
  
      operator Type*() { return &value; }
      operator Type&() { return value; }
      operator Type&&() && { return std::move(value); }
      template <typename T> using cast_op_type = movable_cast_op_type<T>;
  
  private:
      Type value;
  };
  
  // Base class for casting reference/map/block/etc. objects back to python.
  template <typename MapType> struct eigen_map_caster {
  private:
      using props = EigenProps<MapType>;
  
  public:
  
      // Directly referencing a ref/map's data is a bit dangerous (whatever the map/ref points to has
      // to stay around), but we'll allow it under the assumption that you know what you're doing (and
      // have an appropriate keep_alive in place).  We return a numpy array pointing directly at the
      // ref's data (The numpy array ends up read-only if the ref was to a const matrix type.) Note
      // that this means you need to ensure you don't destroy the object in some other way (e.g. with
      // an appropriate keep_alive, or with a reference to a statically allocated matrix).
      static handle cast(const MapType &src, return_value_policy policy, handle parent) {
          switch (policy) {
              case return_value_policy::copy:
                  return eigen_array_cast<props>(src);
              case return_value_policy::reference_internal:
                  return eigen_array_cast<props>(src, parent, is_eigen_mutable_map<MapType>::value);
              case return_value_policy::reference:
              case return_value_policy::automatic:
              case return_value_policy::automatic_reference:
                  return eigen_array_cast<props>(src, none(), is_eigen_mutable_map<MapType>::value);
              default:
                  // move, take_ownership don't make any sense for a ref/map:
                  pybind11_fail("Invalid return_value_policy for Eigen Map/Ref/Block type");
          }
      }
  
      static constexpr auto name = props::descriptor;
  
      // Explicitly delete these: support python -> C++ conversion on these (i.e. these can be return
      // types but not bound arguments).  We still provide them (with an explicitly delete) so that
      // you end up here if you try anyway.
      bool load(handle, bool) = delete;
      operator MapType() = delete;
      template <typename> using cast_op_type = MapType;
  };
  
  // We can return any map-like object (but can only load Refs, specialized next):
  template <typename Type> struct type_caster<Type, enable_if_t<is_eigen_dense_map<Type>::value>>
      : eigen_map_caster<Type> {};
  
  // Loader for Ref<...> arguments.  See the documentation for info on how to make this work without
  // copying (it requires some extra effort in many cases).
  template <typename PlainObjectType, typename StrideType>
  struct type_caster<
      Eigen::Ref<PlainObjectType, 0, StrideType>,
      enable_if_t<is_eigen_dense_map<Eigen::Ref<PlainObjectType, 0, StrideType>>::value>
  > : public eigen_map_caster<Eigen::Ref<PlainObjectType, 0, StrideType>> {
  private:
      using Type = Eigen::Ref<PlainObjectType, 0, StrideType>;
      using props = EigenProps<Type>;
      using Scalar = typename props::Scalar;
      using MapType = Eigen::Map<PlainObjectType, 0, StrideType>;
      using Array = array_t<Scalar, array::forcecast |
                  ((props::row_major ? props::inner_stride : props::outer_stride) == 1 ? array::c_style :
                   (props::row_major ? props::outer_stride : props::inner_stride) == 1 ? array::f_style : 0)>;
      static constexpr bool need_writeable = is_eigen_mutable_map<Type>::value;
      // Delay construction (these have no default constructor)
      std::unique_ptr<MapType> map;
      std::unique_ptr<Type> ref;
      // Our array.  When possible, this is just a numpy array pointing to the source data, but
      // sometimes we can't avoid copying (e.g. input is not a numpy array at all, has an incompatible
      // layout, or is an array of a type that needs to be converted).  Using a numpy temporary
      // (rather than an Eigen temporary) saves an extra copy when we need both type conversion and
      // storage order conversion.  (Note that we refuse to use this temporary copy when loading an
      // argument for a Ref<M> with M non-const, i.e. a read-write reference).
      Array copy_or_ref;
  public:
      bool load(handle src, bool convert) {
          // First check whether what we have is already an array of the right type.  If not, we can't
          // avoid a copy (because the copy is also going to do type conversion).
          bool need_copy = !isinstance<Array>(src);
  
          EigenConformable<props::row_major> fits;
          if (!need_copy) {
              // We don't need a converting copy, but we also need to check whether the strides are
              // compatible with the Ref's stride requirements
              Array aref = reinterpret_borrow<Array>(src);
  
              if (aref && (!need_writeable || aref.writeable())) {
                  fits = props::conformable(aref);
                  if (!fits) return false; // Incompatible dimensions
                  if (!fits.template stride_compatible<props>())
                      need_copy = true;
                  else
                      copy_or_ref = std::move(aref);
              }
              else {
                  need_copy = true;
              }
          }
  
          if (need_copy) {
              // We need to copy: If we need a mutable reference, or we're not supposed to convert
              // (either because we're in the no-convert overload pass, or because we're explicitly
              // instructed not to copy (via `py::arg().noconvert()`) we have to fail loading.
              if (!convert || need_writeable) return false;
  
              Array copy = Array::ensure(src);
              if (!copy) return false;
              fits = props::conformable(copy);
              if (!fits || !fits.template stride_compatible<props>())
                  return false;
              copy_or_ref = std::move(copy);
              loader_life_support::add_patient(copy_or_ref);
          }
  
          ref.reset();
          map.reset(new MapType(data(copy_or_ref), fits.rows, fits.cols, make_stride(fits.stride.outer(), fits.stride.inner())));
          ref.reset(new Type(*map));
  
          return true;
      }
  
      operator Type*() { return ref.get(); }
      operator Type&() { return *ref; }
      template <typename _T> using cast_op_type = pybind11::detail::cast_op_type<_T>;
  
  private:
      template <typename T = Type, enable_if_t<is_eigen_mutable_map<T>::value, int> = 0>
      Scalar *data(Array &a) { return a.mutable_data(); }
  
      template <typename T = Type, enable_if_t<!is_eigen_mutable_map<T>::value, int> = 0>
      const Scalar *data(Array &a) { return a.data(); }
  
      // Attempt to figure out a constructor of `Stride` that will work.
      // If both strides are fixed, use a default constructor:
      template <typename S> using stride_ctor_default = bool_constant<
          S::InnerStrideAtCompileTime != Eigen::Dynamic && S::OuterStrideAtCompileTime != Eigen::Dynamic &&
          std::is_default_constructible<S>::value>;
      // Otherwise, if there is a two-index constructor, assume it is (outer,inner) like
      // Eigen::Stride, and use it:
      template <typename S> using stride_ctor_dual = bool_constant<
          !stride_ctor_default<S>::value && std::is_constructible<S, EigenIndex, EigenIndex>::value>;
      // Otherwise, if there is a one-index constructor, and just one of the strides is dynamic, use
      // it (passing whichever stride is dynamic).
      template <typename S> using stride_ctor_outer = bool_constant<
          !any_of<stride_ctor_default<S>, stride_ctor_dual<S>>::value &&
          S::OuterStrideAtCompileTime == Eigen::Dynamic && S::InnerStrideAtCompileTime != Eigen::Dynamic &&
          std::is_constructible<S, EigenIndex>::value>;
      template <typename S> using stride_ctor_inner = bool_constant<
          !any_of<stride_ctor_default<S>, stride_ctor_dual<S>>::value &&
          S::InnerStrideAtCompileTime == Eigen::Dynamic && S::OuterStrideAtCompileTime != Eigen::Dynamic &&
          std::is_constructible<S, EigenIndex>::value>;
  
      template <typename S = StrideType, enable_if_t<stride_ctor_default<S>::value, int> = 0>
      static S make_stride(EigenIndex, EigenIndex) { return S(); }
      template <typename S = StrideType, enable_if_t<stride_ctor_dual<S>::value, int> = 0>
      static S make_stride(EigenIndex outer, EigenIndex inner) { return S(outer, inner); }
      template <typename S = StrideType, enable_if_t<stride_ctor_outer<S>::value, int> = 0>
      static S make_stride(EigenIndex outer, EigenIndex) { return S(outer); }
      template <typename S = StrideType, enable_if_t<stride_ctor_inner<S>::value, int> = 0>
      static S make_stride(EigenIndex, EigenIndex inner) { return S(inner); }
  
  };
  
  // type_caster for special matrix types (e.g. DiagonalMatrix), which are EigenBase, but not
  // EigenDense (i.e. they don't have a data(), at least not with the usual matrix layout).
  // load() is not supported, but we can cast them into the python domain by first copying to a
  // regular Eigen::Matrix, then casting that.
  template <typename Type>
  struct type_caster<Type, enable_if_t<is_eigen_other<Type>::value>> {
  protected:
      using Matrix = Eigen::Matrix<typename Type::Scalar, Type::RowsAtCompileTime, Type::ColsAtCompileTime>;
      using props = EigenProps<Matrix>;
  public:
      static handle cast(const Type &src, return_value_policy /* policy */, handle /* parent */) {
          handle h = eigen_encapsulate<props>(new Matrix(src));
          return h;
      }
      static handle cast(const Type *src, return_value_policy policy, handle parent) { return cast(*src, policy, parent); }
  
      static constexpr auto name = props::descriptor;
  
      // Explicitly delete these: support python -> C++ conversion on these (i.e. these can be return
      // types but not bound arguments).  We still provide them (with an explicitly delete) so that
      // you end up here if you try anyway.
      bool load(handle, bool) = delete;
      operator Type() = delete;
      template <typename> using cast_op_type = Type;
  };
  
  template<typename Type>
  struct type_caster<Type, enable_if_t<is_eigen_sparse<Type>::value>> {
      typedef typename Type::Scalar Scalar;
      typedef remove_reference_t<decltype(*std::declval<Type>().outerIndexPtr())> StorageIndex;
      typedef typename Type::Index Index;
      static constexpr bool rowMajor = Type::IsRowMajor;
  
      bool load(handle src, bool) {
          if (!src)
              return false;
  
          auto obj = reinterpret_borrow<object>(src);
          object sparse_module = module::import("scipy.sparse");
          object matrix_type = sparse_module.attr(
              rowMajor ? "csr_matrix" : "csc_matrix");
  
          if (!obj.get_type().is(matrix_type)) {
              try {
                  obj = matrix_type(obj);
              } catch (const error_already_set &) {
                  return false;
              }
          }
  
          auto values = array_t<Scalar>((object) obj.attr("data"));
          auto innerIndices = array_t<StorageIndex>((object) obj.attr("indices"));
          auto outerIndices = array_t<StorageIndex>((object) obj.attr("indptr"));
          auto shape = pybind11::tuple((pybind11::object) obj.attr("shape"));
          auto nnz = obj.attr("nnz").cast<Index>();
  
          if (!values || !innerIndices || !outerIndices)
              return false;
  
          value = Eigen::MappedSparseMatrix<Scalar, Type::Flags, StorageIndex>(
              shape[0].cast<Index>(), shape[1].cast<Index>(), nnz,
              outerIndices.mutable_data(), innerIndices.mutable_data(), values.mutable_data());
  
          return true;
      }
  
      static handle cast(const Type &src, return_value_policy /* policy */, handle /* parent */) {
          const_cast<Type&>(src).makeCompressed();
  
          object matrix_type = module::import("scipy.sparse").attr(
              rowMajor ? "csr_matrix" : "csc_matrix");
  
          array data(src.nonZeros(), src.valuePtr());
          array outerIndices((rowMajor ? src.rows() : src.cols()) + 1, src.outerIndexPtr());
          array innerIndices(src.nonZeros(), src.innerIndexPtr());
  
          return matrix_type(
              std::make_tuple(data, innerIndices, outerIndices),
              std::make_pair(src.rows(), src.cols())
          ).release();
      }
  
      PYBIND11_TYPE_CASTER(Type, _<(Type::IsRowMajor) != 0>("scipy.sparse.csr_matrix[", "scipy.sparse.csc_matrix[")
              + npy_format_descriptor<Scalar>::name + _("]"));
  };
  
  PYBIND11_NAMESPACE_END(detail)
  PYBIND11_NAMESPACE_END(PYBIND11_NAMESPACE)
  
  #if defined(__GNUG__) || defined(__clang__)
  #  pragma GCC diagnostic pop
  #elif defined(_MSC_VER)
  #  pragma warning(pop)
  #endif
  