/*===- TableGen'erated file -------------------------------------*- C++ -*-===*\
|*                                                                            *|
|* AttrDef Declarations                                                       *|
|*                                                                            *|
|* Automatically generated file, do not edit!                                 *|
|*                                                                            *|
\*===----------------------------------------------------------------------===*/

#ifdef GET_ATTRDEF_CLASSES
#undef GET_ATTRDEF_CLASSES


namespace mlir {
class AsmParser;
class AsmPrinter;
} // namespace mlir
namespace mlir {
namespace sparse_tensor {
/// An attribute to encode slice information of a sparse tensor on a particular
///     dimension (a tuple of offset, size, stride).
class SparseTensorDimSliceAttr;
/// An attribute to encode information on sparsity properties of tensors, inspired
///     by the TACO formalization of sparse tensors. This encoding is eventually used
///     by a **sparsifier** pass to generate sparse code fully automatically from a
///     sparsity-agnostic representation of the computation, i.e., an implicit sparse
///     representation is converted to an explicit sparse representation where co-iterating
///     loops operate on sparse storage formats rather than tensors with a sparsity
///     encoding. Compiler passes that run before this sparsifier pass need to be aware
///     of the semantics of tensor types with such a sparsity encoding.
/// 
///     In this encoding, we use **dimension** to refer to the axes of the semantic tensor,
///     and **level** to refer to the axes of the actual storage format, i.e., the
///     operational representation of the sparse tensor in memory. The number of
///     dimensions is usually the same as the number of levels (such as CSR storage format).
///     However, the encoding can also map dimensions to higher-order levels (for example,
///     to encode a block-sparse BSR storage format) or to lower-order levels
///     (for example, to linearize dimensions as a single level in the storage).
/// 
///     The encoding contains a map that provides the following:
/// 
///     - An ordered sequence of dimension specifications, each of which defines:
///       - the dimension-size (implicit from the tensor’s dimension-shape)
///       - a **dimension-expression**
///     - An ordered sequence of level specifications, each of which includes a required
///       **level-type**, which defines how the level should be stored. Each level-type
///       consists of:
///       - a **level-expression**, which defines what is stored
///       - a **level-format**
///       - a collection of **level-properties** that apply to the level-format
/// 
///     Each level-expression is an affine expression over dimension-variables. Thus, the
///     level-expressions collectively define an affine map from dimension-coordinates to
///     level-coordinates. The dimension-expressions collectively define the inverse map,
///     which only needs to be provided for elaborate cases where it cannot be inferred
///     automatically.
/// 
///     Each dimension could also have an optional `SparseTensorDimSliceAttr`.
///     Within the sparse storage format, we refer to indices that are stored explicitly
///     as **coordinates** and offsets into the storage format as **positions**.
/// 
///     The supported level-formats are the following:
/// 
///     - **dense** : all entries along this level are stored and linearized.
///     - **batch** : all entries along this level are stored but not linearized.
///     - **compressed** : only nonzeros along this level are stored
///     - **loose_compressed** : as compressed, but allows for free space between regions
///     - **singleton** : a variant of the compressed format, where coordinates have no siblings
///     - **structured[n, m]** : the compression uses a n:m encoding
///       (viz. n out of m consecutive elements are nonzero)
/// 
///     For a compressed level, each position interval is represented in a compact
///     way with a lowerbound `pos(i)` and an upperbound `pos(i+1) - 1`, which implies
///     that successive intervals must appear in order without any "holes" in between
///     them. The loose compressed format relaxes these constraints by representing each
///     position interval with a lowerbound `lo(i)` and an upperbound `hi(i)`, which
///     allows intervals to appear in arbitrary order and with elbow room between them.
/// 
///     By default, each level-type has the property of being unique (no duplicate
///     coordinates at that level) and ordered (coordinates appear sorted at that
///     level). For singleton levels, the coordinates are fused with its parents in AoS
///     (array of structures) scheme. The following properties can be added to a level-format
///     to change this default behavior:
/// 
///     - **nonunique** : duplicate coordinates may appear at the level
///     - **nonordered** : coordinates may appear in arbribratry order
///     - **soa** : only applicable to singleton levels, fuses the singleton
///       level in SoA (structure of arrays) scheme.
/// 
///     In addition to the map, the following fields are optional:
/// 
///     - The required bitwidth for position storage (integral offsets
///       into the sparse storage scheme).  A narrow width reduces the memory
///       footprint of overhead storage, as long as the width suffices to
///       define the total required range (viz. the maximum number of stored
///       entries over all indirection levels).  The choices are `8`, `16`,
///       `32`, `64`, or, the default, `0` to indicate the native bitwidth.
/// 
///     - The required bitwidth for coordinate storage (the coordinates
///       of stored entries).  A narrow width reduces the memory footprint
///       of overhead storage, as long as the width suffices to define
///       the total required range (viz. the maximum value of each tensor
///       coordinate over all levels).  The choices are `8`, `16`, `32`,
///       `64`, or, the default, `0` to indicate a native bitwidth.
/// 
///     - The explicit value for the sparse tensor. If explicitVal is set,
///       then all the non-zero values in the tensor have the same explicit value.
///       The default value Attribute() indicates that it is not set. This
///       is useful for binary-valued sparse tensors whose values can either
///       be an implicit value (0 by default) or an explicit value (such as 1).
///       In this approach, we don't store explicit/implicit values, and metadata
///       (such as position and coordinate arrays) alone fully defines the original tensor.
///       This yields additional savings for the storage requirements,
///       as well as for the computational time, since we skip operating on
///       implicit values and can constant fold the explicit values where they are used.
/// 
///     - The implicit value for the sparse tensor. If implicitVal is set,
///       then the "zero" value in the tensor is equal to the implicit value.
///       For now, we only support `0` as the implicit value but it could be
///       extended in the future. The default value Attribute() indicates that
///       the implicit value is `0` (same type as the tensor element type).
/// 
///     Examples:
/// 
///     ```mlir
///     // Sparse vector.
///     #SparseVector = #sparse_tensor.encoding<{
///       map = (i) -> (i : compressed)
///     }>
///     ... tensor<?xf32, #SparseVector> ...
/// 
///     // Sorted coordinate scheme (arranged in AoS format by default).
///     #SortedCOO = #sparse_tensor.encoding<{
///       map = (i, j) -> (i : compressed(nonunique), j : singleton)
///     }>
///     // coordinates = {x_crd, y_crd}[nnz]
///     ... tensor<?x?xf64, #SortedCOO> ...
/// 
///     // Sorted coordinate scheme (arranged in SoA format).
///     #SortedCOO = #sparse_tensor.encoding<{
///       map = (i, j) -> (i : compressed(nonunique), j : singleton(soa))
///     }>
///     // coordinates = {x_crd[nnz], y_crd[nnz]}
///     ... tensor<?x?xf64, #SortedCOO> ...
/// 
///     // Batched sorted coordinate scheme, with high encoding.
///     #BCOO = #sparse_tensor.encoding<{
///       map = (i, j, k) -> (i : dense, j : compressed(nonunique, high), k : singleton)
///     }>
///     ... tensor<10x10xf32, #BCOO> ...
/// 
///     // Compressed sparse row.
///     #CSR = #sparse_tensor.encoding<{
///       map = (i, j) -> (i : dense, j : compressed)
///     }>
///     ... tensor<100x100xbf16, #CSR> ...
/// 
///     // Doubly compressed sparse column storage with specific bitwidths.
///     #DCSC = #sparse_tensor.encoding<{
///       map = (i, j) -> (j : compressed, i : compressed),
///       posWidth = 32,
///       crdWidth = 8
///     }>
///     ... tensor<8x8xf64, #DCSC> ...
/// 
///     // Doubly compressed sparse column storage with specific
///     // explicit and implicit values.
///     #DCSC = #sparse_tensor.encoding<{
///       map = (i, j) -> (j : compressed, i : compressed),
///       explicitVal = 1 : i64,
///       implicitVal = 0 : i64
///     }>
///     ... tensor<8x8xi64, #DCSC> ...
/// 
///     // Block sparse row storage (2x3 blocks).
///     #BSR = #sparse_tensor.encoding<{
///       map = ( i, j ) ->
///       ( i floordiv 2 : dense,
///         j floordiv 3 : compressed,
///         i mod 2      : dense,
///         j mod 3      : dense
///       )
///     }>
///     ... tensor<20x30xf32, #BSR> ...
/// 
///     // Same block sparse row storage (2x3 blocks) but this time
///     // also with a redundant reverse mapping, which can be inferred.
///     #BSR_explicit = #sparse_tensor.encoding<{
///       map = { ib, jb, ii, jj }
///             ( i = ib * 2 + ii,
///               j = jb * 3 + jj) ->
///       ( ib = i floordiv 2 : dense,
///         jb = j floordiv 3 : compressed,
///         ii = i mod 2 : dense,
///         jj = j mod 3 : dense)
///     }>
///     ... tensor<20x30xf32, #BSR_explicit> ...
/// 
///     // ELL format.
///     // In the simple format for matrix, one array stores values and another
///     // array stores column indices. The arrays have the same number of rows
///     // as the original matrix, but only have as many columns as
///     // the maximum number of nonzeros on a row of the original matrix.
///     // There are many variants for ELL such as jagged diagonal scheme.
///     // To implement ELL, map provides a notion of "counting a
///     // dimension", where every stored element with the same coordinate
///     // is mapped to a new slice. For instance, ELL storage of a 2-d
///     // tensor can be defined with the mapping (i, j) -> (#i, i, j)
///     // using the notation of [Chou20]. Lacking the # symbol in MLIR's
///     // affine mapping, we use a free symbol c to define such counting,
///     // together with a constant that denotes the number of resulting
///     // slices. For example, the mapping [c](i, j) -> (c * 3 * i, i, j)
///     // with the level-types ["dense", "dense", "compressed"] denotes ELL
///     // storage with three jagged diagonals that count the dimension i.
///     #ELL = #sparse_tensor.encoding<{
///       map = [c](i, j) -> (c * 3 * i : dense, i : dense, j : compressed)
///     }>
///     ... tensor<?x?xf64, #ELL> ...
/// 
///     // CSR slice (offset = 0, size = 4, stride = 1 on the first dimension;
///     // offset = 0, size = 8, and a dynamic stride on the second dimension).
///     #CSR_SLICE = #sparse_tensor.encoding<{
///       map = (i : #sparse_tensor<slice(0, 4, 1)>,
///              j : #sparse_tensor<slice(0, 8, ?)>) ->
///             (i : dense, j : compressed)
///     }>
///     ... tensor<?x?xf64, #CSR_SLICE> ...
/// 
///     ```
class SparseTensorEncodingAttr;
/// sparse tensor storage specifier kind
class StorageSpecifierKindAttr;
/// sparse tensor sort algorithm
class SparseTensorSortKindAttr;
/// sparse tensor coordinate translation direction
class CrdTransDirectionKindAttr;
namespace detail {
struct SparseTensorDimSliceAttrStorage;
} // namespace detail
class SparseTensorDimSliceAttr : public ::mlir::Attribute::AttrBase<SparseTensorDimSliceAttr, ::mlir::Attribute, detail::SparseTensorDimSliceAttrStorage> {
public:
  using Base::Base;
  void print(llvm::raw_ostream &os) const;

  /// Special value for dynamic offset/size/stride.
  static constexpr int64_t kDynamic = -1;
  static constexpr bool isDynamic(int64_t v) { return v == kDynamic; }
  static std::optional<uint64_t> getStatic(int64_t v);
  static std::string getStaticString(int64_t v);

  std::optional<uint64_t> getStaticOffset() const;
  std::optional<uint64_t> getStaticStride() const;
  std::optional<uint64_t> getStaticSize() const;
  bool isCompletelyDynamic() const;
  static constexpr ::llvm::StringLiteral name = "sparse_tensor.slice";
  static constexpr ::llvm::StringLiteral dialectName = "sparse_tensor";
  using Base::getChecked;
  static SparseTensorDimSliceAttr get(::mlir::MLIRContext *context, int64_t offset, int64_t size, int64_t stride);
  static SparseTensorDimSliceAttr getChecked(::llvm::function_ref<::mlir::InFlightDiagnostic()> emitError, ::mlir::MLIRContext *context, int64_t offset, int64_t size, int64_t stride);
  static SparseTensorDimSliceAttr get(::mlir::MLIRContext *context);
  static SparseTensorDimSliceAttr getChecked(::llvm::function_ref<::mlir::InFlightDiagnostic()> emitError, ::mlir::MLIRContext *context);
  static ::llvm::LogicalResult verify(::llvm::function_ref<::mlir::InFlightDiagnostic()> emitError, int64_t offset, int64_t size, int64_t stride);
  static ::llvm::LogicalResult verifyInvariants(::llvm::function_ref<::mlir::InFlightDiagnostic()> emitError, int64_t offset, int64_t size, int64_t stride);
  static constexpr ::llvm::StringLiteral getMnemonic() {
    return {"slice"};
  }

  static ::mlir::Attribute parse(::mlir::AsmParser &odsParser, ::mlir::Type odsType);
  void print(::mlir::AsmPrinter &odsPrinter) const;
  int64_t getOffset() const;
  int64_t getSize() const;
  int64_t getStride() const;
};
namespace detail {
struct SparseTensorEncodingAttrStorage;
} // namespace detail
class SparseTensorEncodingAttr : public ::mlir::Attribute::AttrBase<SparseTensorEncodingAttr, ::mlir::Attribute, detail::SparseTensorEncodingAttrStorage, ::mlir::VerifiableTensorEncoding::Trait, ::mlir::OpAsmAttrInterface::Trait> {
public:
  using Base::Base;
  //
  // Factory methods.
  //

  /// Constructs a new encoding with the given dimToLvl mapping,
  /// and all other fields inherited from `this`.
  SparseTensorEncodingAttr withDimToLvl(AffineMap dimToLvl) const;
  SparseTensorEncodingAttr withDimToLvl(SparseTensorEncodingAttr enc) const;

  /// Constructs a new encoding with dimToLvl reset to the default/identity,
  /// and all other fields inherited from `this`.
  SparseTensorEncodingAttr withoutDimToLvl() const;

  /// Constructs a new encoding with the given pointer and index
  /// bitwidths, and all other fields inherited from `this`.
  SparseTensorEncodingAttr withBitWidths(unsigned posWidth, unsigned crdWidth) const;

  /// Constructs a new encoding with the pointer and index bitwidths
  /// reset to the default, and all other fields inherited from `this`.
  SparseTensorEncodingAttr withoutBitWidths() const;

  /// Constructs a new encoding with the given explicit value
  /// and all other fields inherited from `this`.
  SparseTensorEncodingAttr withExplicitVal(Attribute explicitVal) const;

  /// Constructs a new encoding with the explicit value
  /// reset to the default, and all other fields inherited from `this`.
  SparseTensorEncodingAttr withoutExplicitVal() const;

  /// Constructs a new encoding with the given implicit value
  /// and all other fields inherited from `this`.
  SparseTensorEncodingAttr withImplicitVal(Attribute implicitVal) const;

  /// Constructs a new encoding with the implicit value
  /// reset to the default, and all other fields inherited from `this`.
  SparseTensorEncodingAttr withoutImplicitVal() const;

  /// Constructs a new encoding with the given dimSlices, and all
  /// other fields inherited from `this`.
  SparseTensorEncodingAttr withDimSlices(ArrayRef<::mlir::sparse_tensor::SparseTensorDimSliceAttr> dimSlices) const;

  /// Constructs a new encoding with the dimSlices reset to the default,
  /// and all other fields inherited from `this`.
  SparseTensorEncodingAttr withoutDimSlices() const;

  //
  // Rank methods.
  //

  /// Returns the expected number of tensor dimensions.  Asserts that
  /// the encoding is non-null (since no fixed result is valid for every
  /// dense-tensor).
  ::mlir::sparse_tensor::Dimension getDimRank() const;

  /// Returns the number of storage levels.  Asserts that the encoding
  /// is non-null (since no fixed result is valid for every dense-tensor).
  ::mlir::sparse_tensor::Level getLvlRank() const;

  uint64_t getBatchLvlRank() const;

  //
  // lvlTypes methods.
  //

  /// Safely looks up the level-type for the requested level.  (Returns
  /// `LevelType::Dense` for the null encoding, since dense-tensors
  /// are always all-dense.)
  ::mlir::sparse_tensor::LevelType getLvlType(::mlir::sparse_tensor::Level l) const;

  bool isDenseLvl(::mlir::sparse_tensor::Level l) const { return isDenseLT(getLvlType(l)); }
  bool isCompressedLvl(::mlir::sparse_tensor::Level l) const { return isCompressedLT(getLvlType(l)); }
  bool isSingletonLvl(::mlir::sparse_tensor::Level l) const { return isSingletonLT(getLvlType(l)); }
  bool isLooseCompressedLvl(::mlir::sparse_tensor::Level l) const { return isLooseCompressedLT(getLvlType(l)); }
  bool isNOutOfMLvl(::mlir::sparse_tensor::Level l) const { return isNOutOfMLT(getLvlType(l)); }
  bool isOrderedLvl(::mlir::sparse_tensor::Level l) const { return isOrderedLT(getLvlType(l)); }
  bool isUniqueLvl(::mlir::sparse_tensor::Level l) const { return isUniqueLT(getLvlType(l)); }

  /// Returns true if every level is dense.  Also returns true for
  /// the null encoding (since dense-tensors are always all-dense).
  bool isAllDense() const;

  /// Returns true if every level is ordered.  Also returns true for
  /// the null encoding (since dense-tensors are always all-ordered).
  bool isAllOrdered() const;

  //
  // Storage type methods.
  //

  /// Returns the coordinate-overhead MLIR type, defaulting to `IndexType`.
  Type getCrdElemType() const;

  /// Returns the position-overhead MLIR type, defaulting to `IndexType`.
  Type getPosElemType() const;

  /// Returns the coordinate-memref MLIR type, an optional tensorDimShape is
  /// used to refine the leading batch dimensions (if any).
  MemRefType getCrdMemRefType(
    std::optional<ArrayRef<int64_t>> tensorDimShape = std::nullopt) const;

  /// Returns the position-memref MLIR type, an optional tensorDimShape is
  /// used to refine the leading batch dimensions (if any).
  MemRefType getPosMemRefType(
    std::optional<ArrayRef<int64_t>> tensorDimShape = std::nullopt) const;

  //
  // dimToLvl methods.
  //

  /// Returns true if the dimToLvl mapping is the identity.
  /// Also returns true for the null encoding (since dense-tensors
  /// always have the identity mapping).
  bool isIdentity() const;

  /// Returns true if the dimToLvl mapping is a permutation.
  /// Also returns true for the null encoding (since dense-tensors
  /// always have the identity mapping).
  bool isPermutation() const;

  //
  // dimSlices methods.
  //

  bool isSlice() const;

  ::mlir::sparse_tensor::SparseTensorDimSliceAttr getDimSlice(::mlir::sparse_tensor::Dimension dim) const;

  std::optional<uint64_t> getStaticDimSliceOffset(::mlir::sparse_tensor::Dimension dim) const;
  std::optional<uint64_t> getStaticDimSliceStride(::mlir::sparse_tensor::Dimension dim) const;
  std::optional<uint64_t> getStaticLvlSliceOffset(::mlir::sparse_tensor::Level lvl) const;
  std::optional<uint64_t> getStaticLvlSliceStride(::mlir::sparse_tensor::Level lvl) const;

  //
  // Helper function to translate between level/dimension space.
  //

  SmallVector<int64_t> translateShape(::mlir::ArrayRef<int64_t> srcShape, ::mlir::sparse_tensor::CrdTransDirectionKind) const;
  ValueRange translateCrds(::mlir::OpBuilder &builder, ::mlir::Location loc, ::mlir::ValueRange crds, ::mlir::sparse_tensor::CrdTransDirectionKind) const;

  //
  // COO methods.
  //

  /// Returns the starting level of this sparse tensor type for a
  /// trailing COO region that spans **at least** two levels. If
  /// no such COO region is found, then returns the level-rank.
  ///
  /// DEPRECATED: use getCOOSegment instead;
  Level getAoSCOOStart() const;

  /// Returns a list of COO segments in the sparse tensor types.
  SmallVector<COOSegment> getCOOSegments() const;

  //
  // Printing methods.
  //

  void printSymbols(AffineMap &map, AsmPrinter &printer) const;
  void printDimensions(AffineMap &map, AsmPrinter &printer, ArrayRef<::mlir::sparse_tensor::SparseTensorDimSliceAttr> dimSlices) const;
  void printLevels(AffineMap &map, AsmPrinter &printer, ArrayRef<::mlir::sparse_tensor::LevelType> lvlTypes) const;

  //
  // OpAsmAttrInterface methods.
  //

  ::mlir::OpAsmAliasResult getAlias(::llvm::raw_ostream &os) const {
    os << "sparse";
    return ::mlir::OpAsmAliasResult::OverridableAlias;
  }
  static constexpr ::llvm::StringLiteral name = "sparse_tensor.encoding";
  static constexpr ::llvm::StringLiteral dialectName = "sparse_tensor";
  using Base::getChecked;
  static SparseTensorEncodingAttr get(::mlir::MLIRContext *context, ::llvm::ArrayRef<::mlir::sparse_tensor::LevelType> lvlTypes, AffineMap dimToLvl, AffineMap lvlToDim, unsigned posWidth, unsigned crdWidth, ::mlir::Attribute explicitVal, ::mlir::Attribute implicitVal, ::llvm::ArrayRef<::mlir::sparse_tensor::SparseTensorDimSliceAttr> dimSlices);
  static SparseTensorEncodingAttr getChecked(::llvm::function_ref<::mlir::InFlightDiagnostic()> emitError, ::mlir::MLIRContext *context, ::llvm::ArrayRef<::mlir::sparse_tensor::LevelType> lvlTypes, AffineMap dimToLvl, AffineMap lvlToDim, unsigned posWidth, unsigned crdWidth, ::mlir::Attribute explicitVal, ::mlir::Attribute implicitVal, ::llvm::ArrayRef<::mlir::sparse_tensor::SparseTensorDimSliceAttr> dimSlices);
  static SparseTensorEncodingAttr get(::mlir::MLIRContext *context, ArrayRef<::mlir::sparse_tensor::LevelType> lvlTypes, AffineMap dimToLvl = {}, AffineMap lvlToDim = {}, unsigned posWidth = 0, unsigned crdWidth = 0, ::mlir::Attribute explicitVal = {}, ::mlir::Attribute implicitVal = {});
  static SparseTensorEncodingAttr getChecked(::llvm::function_ref<::mlir::InFlightDiagnostic()> emitError, ::mlir::MLIRContext *context, ArrayRef<::mlir::sparse_tensor::LevelType> lvlTypes, AffineMap dimToLvl = {}, AffineMap lvlToDim = {}, unsigned posWidth = 0, unsigned crdWidth = 0, ::mlir::Attribute explicitVal = {}, ::mlir::Attribute implicitVal = {});
  static ::llvm::LogicalResult verify(::llvm::function_ref<::mlir::InFlightDiagnostic()> emitError, ::llvm::ArrayRef<::mlir::sparse_tensor::LevelType> lvlTypes, AffineMap dimToLvl, AffineMap lvlToDim, unsigned posWidth, unsigned crdWidth, ::mlir::Attribute explicitVal, ::mlir::Attribute implicitVal, ::llvm::ArrayRef<::mlir::sparse_tensor::SparseTensorDimSliceAttr> dimSlices);
  static ::llvm::LogicalResult verifyInvariants(::llvm::function_ref<::mlir::InFlightDiagnostic()> emitError, ::llvm::ArrayRef<::mlir::sparse_tensor::LevelType> lvlTypes, AffineMap dimToLvl, AffineMap lvlToDim, unsigned posWidth, unsigned crdWidth, ::mlir::Attribute explicitVal, ::mlir::Attribute implicitVal, ::llvm::ArrayRef<::mlir::sparse_tensor::SparseTensorDimSliceAttr> dimSlices);
  static constexpr ::llvm::StringLiteral getMnemonic() {
    return {"encoding"};
  }

  static ::mlir::Attribute parse(::mlir::AsmParser &odsParser, ::mlir::Type odsType);
  void print(::mlir::AsmPrinter &odsPrinter) const;
  ::llvm::ArrayRef<::mlir::sparse_tensor::LevelType> getLvlTypes() const;
  AffineMap getDimToLvl() const;
  AffineMap getLvlToDim() const;
  unsigned getPosWidth() const;
  unsigned getCrdWidth() const;
  ::mlir::Attribute getExplicitVal() const;
  ::mlir::Attribute getImplicitVal() const;
  ::llvm::ArrayRef<::mlir::sparse_tensor::SparseTensorDimSliceAttr> getDimSlices() const;
  ::llvm::LogicalResult verifyEncoding(::mlir::ArrayRef<int64_t> shape, ::mlir::Type elementType, ::llvm::function_ref<::mlir::InFlightDiagnostic()> emitError) const;
};
namespace detail {
struct StorageSpecifierKindAttrStorage;
} // namespace detail
class StorageSpecifierKindAttr : public ::mlir::Attribute::AttrBase<StorageSpecifierKindAttr, ::mlir::Attribute, detail::StorageSpecifierKindAttrStorage> {
public:
  using Base::Base;
  static constexpr ::llvm::StringLiteral name = "sparse_tensor.kind";
  static constexpr ::llvm::StringLiteral dialectName = "sparse_tensor";
  static StorageSpecifierKindAttr get(::mlir::MLIRContext *context, ::mlir::sparse_tensor::StorageSpecifierKind value);
  static constexpr ::llvm::StringLiteral getMnemonic() {
    return {"kind"};
  }

  static ::mlir::Attribute parse(::mlir::AsmParser &odsParser, ::mlir::Type odsType);
  void print(::mlir::AsmPrinter &odsPrinter) const;
  ::mlir::sparse_tensor::StorageSpecifierKind getValue() const;
};
namespace detail {
struct SparseTensorSortKindAttrStorage;
} // namespace detail
class SparseTensorSortKindAttr : public ::mlir::Attribute::AttrBase<SparseTensorSortKindAttr, ::mlir::Attribute, detail::SparseTensorSortKindAttrStorage> {
public:
  using Base::Base;
  static constexpr ::llvm::StringLiteral name = "sparse_tensor.SparseTensorSortAlgorithm";
  static constexpr ::llvm::StringLiteral dialectName = "sparse_tensor";
  static SparseTensorSortKindAttr get(::mlir::MLIRContext *context, ::mlir::sparse_tensor::SparseTensorSortKind value);
  static constexpr ::llvm::StringLiteral getMnemonic() {
    return {"SparseTensorSortAlgorithm"};
  }

  static ::mlir::Attribute parse(::mlir::AsmParser &odsParser, ::mlir::Type odsType);
  void print(::mlir::AsmPrinter &odsPrinter) const;
  ::mlir::sparse_tensor::SparseTensorSortKind getValue() const;
};
namespace detail {
struct CrdTransDirectionKindAttrStorage;
} // namespace detail
class CrdTransDirectionKindAttr : public ::mlir::Attribute::AttrBase<CrdTransDirectionKindAttr, ::mlir::Attribute, detail::CrdTransDirectionKindAttrStorage> {
public:
  using Base::Base;
  static constexpr ::llvm::StringLiteral name = "sparse_tensor.CrdTransDirection";
  static constexpr ::llvm::StringLiteral dialectName = "sparse_tensor";
  static CrdTransDirectionKindAttr get(::mlir::MLIRContext *context, ::mlir::sparse_tensor::CrdTransDirectionKind value);
  static constexpr ::llvm::StringLiteral getMnemonic() {
    return {"CrdTransDirection"};
  }

  static ::mlir::Attribute parse(::mlir::AsmParser &odsParser, ::mlir::Type odsType);
  void print(::mlir::AsmPrinter &odsPrinter) const;
  ::mlir::sparse_tensor::CrdTransDirectionKind getValue() const;
};
} // namespace sparse_tensor
} // namespace mlir
MLIR_DECLARE_EXPLICIT_TYPE_ID(::mlir::sparse_tensor::SparseTensorDimSliceAttr)
MLIR_DECLARE_EXPLICIT_TYPE_ID(::mlir::sparse_tensor::SparseTensorEncodingAttr)
MLIR_DECLARE_EXPLICIT_TYPE_ID(::mlir::sparse_tensor::StorageSpecifierKindAttr)
MLIR_DECLARE_EXPLICIT_TYPE_ID(::mlir::sparse_tensor::SparseTensorSortKindAttr)
MLIR_DECLARE_EXPLICIT_TYPE_ID(::mlir::sparse_tensor::CrdTransDirectionKindAttr)

#endif  // GET_ATTRDEF_CLASSES

