//===----------------------------------------------------------------------===//
//
// Part of the LLVM Project, under the Apache License v2.0 with LLVM Exceptions.
// See https://llvm.org/LICENSE.txt for license information.
// SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
//
//===----------------------------------------------------------------------===//

#include "mlir/Dialect/Arithmetic/IR/Arithmetic.h"
#include "mlir/Dialect/StandardOps/Utils/Utils.h"
#include "mlir/Dialect/Tensor/IR/Tensor.h"
#include "mlir/Dialect/Utils/ReshapeOpsUtils.h"
#include "mlir/Dialect/Utils/StaticValueUtils.h"
#include "mlir/IR/BlockAndValueMapping.h"
#include "mlir/IR/Builders.h"
#include "mlir/IR/BuiltinAttributeInterfaces.h"
#include "mlir/IR/Matchers.h"
#include "mlir/IR/PatternMatch.h"
#include "mlir/IR/TypeUtilities.h"
#include "llvm/ADT/STLExtras.h"

using namespace mlir;
using namespace mlir::tensor;

/// Materialize a single constant operation from a given attribute value with
/// the desired resultant type.
Operation *TensorDialect::materializeConstant(OpBuilder &builder,
                                              Attribute value, Type type,
                                              Location loc) {
  if (arith::ConstantOp::isBuildableWith(value, type))
    return builder.create<arith::ConstantOp>(loc, value, type);
  if (ConstantOp::isBuildableWith(value, type))
    return builder.create<ConstantOp>(loc, value, type);
  return nullptr;
}

//===----------------------------------------------------------------------===//
// CastOp
//===----------------------------------------------------------------------===//

/// Returns true if `target` is a ranked tensor type that preserves static
/// information available in the `source` ranked tensor type.
bool mlir::tensor::preservesStaticInformation(Type source, Type target) {
  auto sourceType = source.dyn_cast<RankedTensorType>();
  auto targetType = target.dyn_cast<RankedTensorType>();

  // Requires RankedTensorType.
  if (!sourceType || !targetType)
    return false;

  // Requires same elemental type.
  if (sourceType.getElementType() != targetType.getElementType())
    return false;

  // Requires same rank.
  if (sourceType.getRank() != targetType.getRank())
    return false;

  // If cast is towards more static sizes along any dimension, don't fold.
  for (auto t : llvm::zip(sourceType.getShape(), targetType.getShape())) {
    if (!ShapedType::isDynamic(std::get<0>(t)) &&
        ShapedType::isDynamic(std::get<1>(t)))
      return false;
  }

  return true;
}

/// Determines whether tensor::CastOp casts to a more dynamic version of the
/// source tensor. This is useful to fold a tensor.cast into a consuming op and
/// implement canonicalization patterns for ops in different dialects that may
/// consume the results of tensor.cast operations. Such foldable tensor.cast
/// operations are typically inserted as `slice` ops and are canonicalized,
/// to preserve the type compatibility of their uses.
///
/// Returns true when all conditions are met:
/// 1. source and result are ranked tensors with same element type and rank.
/// 2. the tensor type has more static information than the result
///
/// Example:
/// ```mlir
///   %1 = tensor.cast %0 : tensor<8x16xf32> to tensor<?x?xf32>
///   %2 = consumer %1 ... : tensor<?x?xf32> ...
/// ```
///
/// folds into:
///
/// ```mlir
///   %2 = consumer %0 ... : tensor<8x16xf32> ...
/// ```
bool mlir::tensor::canFoldIntoConsumerOp(CastOp castOp) {
  if (!castOp)
    return false;

  // Can fold if the source of cast has at least as much static information as
  // its results.
  return preservesStaticInformation(castOp.getType(),
                                    castOp.source().getType());
}

/// Performs folding of any operand of `op` if it comes from a tensor::CastOp
/// that can be folded.
LogicalResult mlir::tensor::foldTensorCast(Operation *op) {
  bool folded = false;
  for (OpOperand &operand : op->getOpOperands()) {
    auto castOp = operand.get().getDefiningOp<tensor::CastOp>();
    if (castOp && tensor::canFoldIntoConsumerOp(castOp)) {
      operand.set(castOp.getOperand());
      folded = true;
    }
  }
  return success(folded);
}

bool CastOp::areCastCompatible(TypeRange inputs, TypeRange outputs) {
  if (inputs.size() != 1 || outputs.size() != 1)
    return false;
  Type a = inputs.front(), b = outputs.front();
  auto aT = a.dyn_cast<TensorType>();
  auto bT = b.dyn_cast<TensorType>();
  if (!aT || !bT)
    return false;

  if (aT.getElementType() != bT.getElementType())
    return false;

  return succeeded(verifyCompatibleShape(aT, bT));
}

/// Compute a TensorType that has the joined shape knowledge of the two
/// given TensorTypes. The element types need to match.
static TensorType joinShapes(TensorType one, TensorType two) {
  assert(one.getElementType() == two.getElementType());

  if (!one.hasRank())
    return two;
  if (!two.hasRank())
    return one;

  int64_t rank = one.getRank();
  if (rank != two.getRank())
    return {};

  SmallVector<int64_t, 4> join;
  join.reserve(rank);
  for (int64_t i = 0; i < rank; ++i) {
    if (one.isDynamicDim(i)) {
      join.push_back(two.getDimSize(i));
      continue;
    }
    if (two.isDynamicDim(i)) {
      join.push_back(one.getDimSize(i));
      continue;
    }
    if (one.getDimSize(i) != two.getDimSize(i))
      return {};
    join.push_back(one.getDimSize(i));
  }
  return RankedTensorType::get(join, one.getElementType());
}

namespace {

/// Replaces chains of two tensor.cast operations by a single tensor.cast
/// operation if doing so does not remove runtime constraints.
struct ChainedTensorCast : public OpRewritePattern<CastOp> {
  using OpRewritePattern<CastOp>::OpRewritePattern;

  LogicalResult matchAndRewrite(CastOp tensorCast,
                                PatternRewriter &rewriter) const final {
    auto tensorCastOperand = tensorCast.getOperand().getDefiningOp<CastOp>();

    if (!tensorCastOperand)
      return failure();

    auto sourceType =
        tensorCastOperand.getOperand().getType().cast<TensorType>();
    auto intermediateType = tensorCastOperand.getType().cast<TensorType>();
    auto resultType = tensorCast.getType().cast<TensorType>();

    // We can remove the intermediate cast if joining all three produces the
    // same result as just joining the source and result shapes.
    auto firstJoin =
        joinShapes(joinShapes(sourceType, intermediateType), resultType);

    // The join might not exist if the cast sequence would fail at runtime.
    if (!firstJoin)
      return failure();

    // The newJoin always exists if the above join exists, it might just contain
    // less information. If so, we cannot drop the intermediate cast, as doing
    // so would remove runtime checks.
    auto newJoin = joinShapes(sourceType, resultType);
    if (firstJoin != newJoin)
      return failure();

    rewriter.replaceOpWithNewOp<CastOp>(tensorCast, resultType,
                                        tensorCastOperand.getOperand());
    return success();
  }
};

} // namespace

void CastOp::getCanonicalizationPatterns(RewritePatternSet &results,
                                         MLIRContext *context) {
  results.add<ChainedTensorCast>(context);
}

//===----------------------------------------------------------------------===//
// DimOp
//===----------------------------------------------------------------------===//

void DimOp::build(OpBuilder &builder, OperationState &result, Value source,
                  int64_t index) {
  auto loc = result.location;
  Value indexValue = builder.create<arith::ConstantIndexOp>(loc, index);
  build(builder, result, source, indexValue);
}

Optional<int64_t> DimOp::getConstantIndex() {
  if (auto constantOp = index().getDefiningOp<arith::ConstantOp>())
    return constantOp.getValue().cast<IntegerAttr>().getInt();
  return {};
}

static LogicalResult verify(DimOp op) {
  // Assume unknown index to be in range.
  Optional<int64_t> index = op.getConstantIndex();
  if (!index.hasValue())
    return success();

  // Check that constant index is not knowingly out of range.
  auto type = op.source().getType();
  if (auto tensorType = type.dyn_cast<RankedTensorType>()) {
    if (index.getValue() >= tensorType.getRank())
      return op.emitOpError("index is out of range");
  } else if (type.isa<UnrankedTensorType>()) {
    // Assume index to be in range.
  } else {
    llvm_unreachable("expected operand with tensor type");
  }
  return success();
}

OpFoldResult DimOp::fold(ArrayRef<Attribute> operands) {
  // All forms of folding require a known index.
  auto index = operands[1].dyn_cast_or_null<IntegerAttr>();
  if (!index)
    return {};

  // Folding for unranked types (UnrankedTensorType) is not supported.
  auto tensorType = source().getType().dyn_cast<RankedTensorType>();
  if (!tensorType)
    return {};

  // Fold if the shape extent along the given index is known.
  if (!tensorType.isDynamicDim(index.getInt())) {
    Builder builder(getContext());
    return builder.getIndexAttr(tensorType.getShape()[index.getInt()]);
  }

  Operation *definingOp = source().getDefiningOp();

  // Fold dim to the operand of tensor.generate.
  if (auto fromElements = dyn_cast_or_null<tensor::GenerateOp>(definingOp)) {
    auto resultType =
        fromElements.getResult().getType().cast<RankedTensorType>();
    // The case where the type encodes the size of the dimension is handled
    // above.
    assert(resultType.getShape()[index.getInt()] ==
           RankedTensorType::kDynamicSize);

    // Find the operand of the fromElements that corresponds to this index.
    auto dynExtents = fromElements.dynamicExtents().begin();
    for (auto dim : resultType.getShape().take_front(index.getInt()))
      if (dim == RankedTensorType::kDynamicSize)
        dynExtents++;

    return Value{*dynExtents};
  }

  // The size at the given index is now known to be a dynamic size.
  unsigned unsignedIndex = index.getValue().getZExtValue();

  if (auto sliceOp = dyn_cast_or_null<tensor::ExtractSliceOp>(definingOp)) {
    // Fold only for non-rank reduced ops. For the rank-reduced version, rely on
    // `resolve-shaped-type-result-dims` pass.
    if (sliceOp.getType().getRank() == sliceOp.getSourceType().getRank() &&
        sliceOp.isDynamicSize(unsignedIndex)) {
      return {sliceOp.getDynamicSize(unsignedIndex)};
    }
  }

  // dim(cast) -> dim
  if (succeeded(foldTensorCast(*this)))
    return getResult();

  return {};
}

namespace {
/// Fold dim of a cast into the dim of the source of the tensor cast.
struct DimOfCastOp : public OpRewritePattern<DimOp> {
  using OpRewritePattern<DimOp>::OpRewritePattern;

  LogicalResult matchAndRewrite(DimOp dimOp,
                                PatternRewriter &rewriter) const override {
    auto castOp = dimOp.source().getDefiningOp<CastOp>();
    if (!castOp)
      return failure();
    Value newSource = castOp.getOperand();
    rewriter.replaceOpWithNewOp<DimOp>(dimOp, newSource, dimOp.index());
    return success();
  }
};
} // namespace

void DimOp::getCanonicalizationPatterns(RewritePatternSet &results,
                                        MLIRContext *context) {
  results.add<DimOfCastOp>(context);
}

//===----------------------------------------------------------------------===//
// ExtractOp
//===----------------------------------------------------------------------===//

static LogicalResult verify(ExtractOp op) {
  // Verify the # indices match if we have a ranked type.
  if (auto tensorType = op.tensor().getType().dyn_cast<RankedTensorType>())
    if (tensorType.getRank() != static_cast<int64_t>(op.indices().size()))
      return op.emitOpError("incorrect number of indices for extract_element");

  return success();
}

OpFoldResult ExtractOp::fold(ArrayRef<Attribute> operands) {
  // The tensor operand must be a known constant.
  Attribute tensor = operands.front();
  if (!tensor)
    return {};
  // If this is a splat elements attribute, simply return the value. All of the
  // elements of a splat attribute are the same.
  if (auto splatTensor = tensor.dyn_cast<SplatElementsAttr>())
    return splatTensor.getSplatValue<Attribute>();

  // Otherwise, collect the constant indices into the tensor.
  SmallVector<uint64_t, 8> indices;
  for (Attribute indice : llvm::drop_begin(operands, 1)) {
    if (!indice || !indice.isa<IntegerAttr>())
      return {};
    indices.push_back(indice.cast<IntegerAttr>().getInt());
  }

  // If this is an elements attribute, query the value at the given indices.
  auto elementsAttr = tensor.dyn_cast<ElementsAttr>();
  if (elementsAttr && elementsAttr.isValidIndex(indices))
    return elementsAttr.getValues<Attribute>()[indices];
  return {};
}

//===----------------------------------------------------------------------===//
// FromElementsOp
//===----------------------------------------------------------------------===//

void FromElementsOp::build(OpBuilder &builder, OperationState &result,
                           Type elementType, ValueRange elements) {
  Type resultTy = RankedTensorType::get({static_cast<int64_t>(elements.size())},
                                        elementType);
  result.addOperands(elements);
  result.addTypes(resultTy);
}

void FromElementsOp::build(OpBuilder &builder, OperationState &result,
                           ValueRange elements) {
  assert(!elements.empty() && "expected at least one element");
  build(builder, result, elements.front().getType(), elements);
}

OpFoldResult FromElementsOp::fold(ArrayRef<Attribute> operands) {
  if (!llvm::is_contained(operands, nullptr))
    return DenseElementsAttr::get(getType(), operands);
  return {};
}

namespace {

// Canonicalizes the pattern of the form
//
// %tensor = tensor.from_elements(%element) : (i32) -> tensor<1xi32>
// %extracted_element = tensor.extract %tensor[%c0] : tensor<1xi32>
//
// to just %element.
struct ExtractElementFromTensorFromElements
    : public OpRewritePattern<tensor::ExtractOp> {
  using OpRewritePattern<tensor::ExtractOp>::OpRewritePattern;

  LogicalResult matchAndRewrite(tensor::ExtractOp extract,
                                PatternRewriter &rewriter) const final {
    if (extract.indices().size() != 1)
      return failure();

    auto tensorFromElements = extract.tensor().getDefiningOp<FromElementsOp>();
    if (tensorFromElements == nullptr)
      return failure();

    APInt index;
    if (!matchPattern(*extract.indices().begin(), m_ConstantInt(&index)))
      return failure();
    // Prevent out of bounds accesses. This can happen in invalid code that will
    // never execute.
    if (tensorFromElements->getNumOperands() <= index.getZExtValue() ||
        index.getSExtValue() < 0)
      return failure();
    rewriter.replaceOp(extract,
                       tensorFromElements.getOperand(index.getZExtValue()));
    return success();
  }
};

} // namespace

void FromElementsOp::getCanonicalizationPatterns(RewritePatternSet &results,
                                                 MLIRContext *context) {
  results.add<ExtractElementFromTensorFromElements>(context);
}

//===----------------------------------------------------------------------===//
// InsertOp
//===----------------------------------------------------------------------===//

static LogicalResult verify(InsertOp op) {
  // Verify the # indices match if we have a ranked type.
  if (auto destType = op.dest().getType().dyn_cast<RankedTensorType>())
    if (destType.getRank() != static_cast<int64_t>(op.indices().size()))
      return op.emitOpError("incorrect number of indices");
  return success();
}

OpFoldResult InsertOp::fold(ArrayRef<Attribute> operands) {
  Attribute scalar = operands[0];
  Attribute dest = operands[1];
  if (scalar && dest)
    if (auto splatDest = dest.dyn_cast<SplatElementsAttr>())
      if (scalar == splatDest.getSplatValue<Attribute>())
        return dest;
  return {};
}

//===----------------------------------------------------------------------===//
// GenerateOp
//===----------------------------------------------------------------------===//

static LogicalResult verify(GenerateOp op) {
  // Ensure that the tensor type has as many dynamic dimensions as are specified
  // by the operands.
  RankedTensorType resultTy = op.getType().cast<RankedTensorType>();
  if (op.getNumOperands() != resultTy.getNumDynamicDims())
    return op.emitError("must have as many index operands as dynamic extents "
                        "in the result type");

  // Ensure that region arguments span the index space.
  if (!llvm::all_of(op.body().getArgumentTypes(),
                    [](Type ty) { return ty.isIndex(); }))
    return op.emitError("all body arguments must be index");
  if (op.body().getNumArguments() != resultTy.getRank())
    return op.emitError("must have one body argument per input dimension");

  // Ensure that the region yields an element of the right type.
  auto yieldOp =
      llvm::cast<YieldOp>(op.body().getBlocks().front().getTerminator());
  if (yieldOp.value().getType() != resultTy.getElementType())
    return op.emitOpError(
        "body must be terminated with a `yield` operation of the tensor "
        "element type");

  return success();
}

void GenerateOp::build(
    OpBuilder &b, OperationState &result, Type resultTy,
    ValueRange dynamicExtents,
    function_ref<void(OpBuilder &, Location, ValueRange)> bodyBuilder) {
  build(b, result, resultTy, dynamicExtents);

  // Build and populate body.
  OpBuilder::InsertionGuard guard(b);
  Region *bodyRegion = result.regions.front().get();
  auto rank = resultTy.cast<RankedTensorType>().getRank();
  SmallVector<Type, 2> argumentTypes(rank, b.getIndexType());
  Block *bodyBlock =
      b.createBlock(bodyRegion, bodyRegion->end(), argumentTypes);
  bodyBuilder(b, result.location, bodyBlock->getArguments());
}

namespace {

/// Canonicalizes tensor.generate operations with a constant
/// operand into the equivalent operation with the operand expressed in the
/// result type, instead. We also insert a type cast to make sure that the
/// resulting IR is still well-typed.
struct StaticTensorGenerate : public OpRewritePattern<GenerateOp> {
  using OpRewritePattern<GenerateOp>::OpRewritePattern;

  LogicalResult matchAndRewrite(GenerateOp tensorFromElements,
                                PatternRewriter &rewriter) const final {
    auto resultType =
        tensorFromElements.getResult().getType().cast<RankedTensorType>();

    if (resultType.hasStaticShape())
      return failure();

    SmallVector<Value, 4> newOperands;
    SmallVector<int64_t, 4> newShape;
    auto operandsIt = tensorFromElements.dynamicExtents().begin();

    for (int64_t dim : resultType.getShape()) {
      if (dim != RankedTensorType::kDynamicSize) {
        newShape.push_back(dim);
        continue;
      }
      APInt index;
      if (!matchPattern(*operandsIt, m_ConstantInt(&index))) {
        newShape.push_back(RankedTensorType::kDynamicSize);
        newOperands.push_back(*operandsIt++);
        continue;
      }
      newShape.push_back(index.getSExtValue());
      operandsIt++;
    }

    if (newOperands.size() == tensorFromElements.dynamicExtents().size())
      return failure();

    auto loc = tensorFromElements.getLoc();
    auto newOp = rewriter.create<GenerateOp>(
        loc, RankedTensorType::get(newShape, resultType.getElementType()),
        newOperands);
    rewriter.inlineRegionBefore(tensorFromElements.body(), newOp.body(),
                                newOp.body().begin());
    rewriter.replaceOpWithNewOp<tensor::CastOp>(tensorFromElements, resultType,
                                                newOp);
    return success();
  }
};

/// Canonicalizes the pattern of the form
///
/// %tensor = tensor.generate %x {
///   ^bb0(%arg0: index):  // no predecessors
///   <computation>
///   yield %1 : index
/// } : tensor<?xindex>
/// %extracted_element = tensor.extract %tensor[%c0] : tensor<?xi32>
///
/// to just <computation> with %arg0 replaced by %c0. We only do this if the
/// tensor.generate operation has no side-effects.
struct ExtractFromTensorGenerate : public OpRewritePattern<tensor::ExtractOp> {
  using OpRewritePattern<tensor::ExtractOp>::OpRewritePattern;

  LogicalResult matchAndRewrite(tensor::ExtractOp extract,
                                PatternRewriter &rewriter) const final {
    auto tensorFromElements = extract.tensor().getDefiningOp<GenerateOp>();
    if (!tensorFromElements || !wouldOpBeTriviallyDead(tensorFromElements))
      return failure();

    BlockAndValueMapping mapping;
    Block *body = tensorFromElements.getBody();
    mapping.map(body->getArguments(), extract.indices());
    for (auto &op : body->without_terminator())
      rewriter.clone(op, mapping);

    auto yield = cast<YieldOp>(body->getTerminator());

    rewriter.replaceOp(extract, mapping.lookupOrDefault(yield.value()));
    return success();
  }
};

/// Canonicalizes the pattern of the form
///
/// %val = tensor.cast %source : : tensor<?xi32> to tensor<2xi32>
/// %extracted_element = tensor.extract %val[%c0] : tensor<2xi32>
///
/// to
///
/// %extracted_element = tensor.extract %source[%c0] : tensor<?xi32>
struct ExtractFromTensorCast : public OpRewritePattern<tensor::ExtractOp> {
  using OpRewritePattern<tensor::ExtractOp>::OpRewritePattern;

  LogicalResult matchAndRewrite(tensor::ExtractOp extract,
                                PatternRewriter &rewriter) const final {
    auto tensorCast = extract.tensor().getDefiningOp<tensor::CastOp>();
    if (!tensorCast)
      return failure();

    rewriter.replaceOpWithNewOp<tensor::ExtractOp>(extract, tensorCast.source(),
                                                   extract.indices());
    return success();
  }
};

} // namespace

void GenerateOp::getCanonicalizationPatterns(RewritePatternSet &results,
                                             MLIRContext *context) {
  // TODO: Move extract patterns to tensor::ExtractOp.
  results.add<ExtractFromTensorGenerate, ExtractFromTensorCast,
              StaticTensorGenerate>(context);
}

//===----------------------------------------------------------------------===//
// ReshapeOp
//===----------------------------------------------------------------------===//

static int64_t GetNumElements(ShapedType type) {
  int64_t numElements = 1;
  for (auto dim : type.getShape())
    numElements *= dim;
  return numElements;
}

static LogicalResult verify(ReshapeOp op) {
  TensorType operandType = op.source().getType().cast<TensorType>();
  TensorType resultType = op.result().getType().cast<TensorType>();

  if (operandType.getElementType() != resultType.getElementType())
    return op.emitOpError("element types of source and destination tensor "
                          "types should be the same");

  int64_t shapeSize =
      op.shape().getType().cast<RankedTensorType>().getDimSize(0);
  auto resultRankedType = resultType.dyn_cast<RankedTensorType>();
  auto operandRankedType = operandType.dyn_cast<RankedTensorType>();

  if (resultRankedType) {
    if (operandRankedType && resultRankedType.hasStaticShape() &&
        operandRankedType.hasStaticShape()) {
      if (GetNumElements(operandRankedType) != GetNumElements(resultRankedType))
        return op.emitOpError("source and destination tensor should have the "
                              "same number of elements");
    }
    if (shapeSize == TensorType::kDynamicSize)
      return op.emitOpError("cannot use shape operand with dynamic length to "
                            "reshape to statically-ranked tensor type");
    if (shapeSize != resultRankedType.getRank())
      return op.emitOpError(
          "length of shape operand differs from the result's tensor rank");
  }
  return success();
}

//===----------------------------------------------------------------------===//
// Reassociative reshape ops
//===----------------------------------------------------------------------===//

SmallVector<AffineMap, 4> CollapseShapeOp::getReassociationMaps() {
  return getSymbolLessAffineMaps(getReassociationExprs());
}
SmallVector<ReassociationExprs, 4> CollapseShapeOp::getReassociationExprs() {
  return convertReassociationIndicesToExprs(getContext(),
                                            getReassociationIndices());
}

SmallVector<AffineMap, 4> ExpandShapeOp::getReassociationMaps() {
  return getSymbolLessAffineMaps(getReassociationExprs());
}
SmallVector<ReassociationExprs, 4> ExpandShapeOp::getReassociationExprs() {
  return convertReassociationIndicesToExprs(getContext(),
                                            getReassociationIndices());
}

static void print(OpAsmPrinter &p, ExpandShapeOp op) {
  ::mlir::printReshapeOp<ExpandShapeOp>(p, op);
}

static void print(OpAsmPrinter &p, CollapseShapeOp op) {
  ::mlir::printReshapeOp<CollapseShapeOp>(p, op);
}

/// Compute the RankedTensorType obtained by applying `reassociation` to `type`.
static RankedTensorType
computeTensorReshapeCollapsedType(RankedTensorType type,
                                  ArrayRef<AffineMap> reassociation) {
  auto shape = type.getShape();
  SmallVector<int64_t, 4> newShape;
  newShape.reserve(reassociation.size());

  // Use the fact that reassociation is valid to simplify the logic: only use
  // each map's rank.
  assert(isReassociationValid(reassociation) && "invalid reassociation");
  unsigned currentDim = 0;
  for (AffineMap m : reassociation) {
    unsigned dim = m.getNumResults();
    auto band = shape.slice(currentDim, dim);
    int64_t size = 1;
    if (llvm::is_contained(band, ShapedType::kDynamicSize))
      size = ShapedType::kDynamicSize;
    else
      for (unsigned d = 0; d < dim; ++d)
        size *= shape[currentDim + d];
    newShape.push_back(size);
    currentDim += dim;
  }

  return RankedTensorType::get(newShape, type.getElementType());
}

void CollapseShapeOp::build(OpBuilder &b, OperationState &result, Value src,
                            ArrayRef<ReassociationIndices> reassociation,
                            ArrayRef<NamedAttribute> attrs) {
  auto resultType = computeTensorReshapeCollapsedType(
      src.getType().cast<RankedTensorType>(),
      getSymbolLessAffineMaps(
          convertReassociationIndicesToExprs(b.getContext(), reassociation)));
  build(b, result, resultType, src, attrs);
  result.addAttribute(getReassociationAttrName(),
                      getReassociationIndicesAttribute(b, reassociation));
}

void ExpandShapeOp::build(OpBuilder &b, OperationState &result, Value src,
                          ArrayRef<ReassociationIndices> reassociation,
                          ArrayRef<NamedAttribute> attrs) {
  auto resultType = computeTensorReshapeCollapsedType(
      src.getType().cast<RankedTensorType>(),
      getSymbolLessAffineMaps(
          convertReassociationIndicesToExprs(b.getContext(), reassociation)));
  build(b, result, resultType, src, attrs);
  result.addAttribute(getReassociationAttrName(),
                      getReassociationIndicesAttribute(b, reassociation));
}

template <typename TensorReshapeOp, bool isExpansion = std::is_same<
                                        TensorReshapeOp, ExpandShapeOp>::value>
static LogicalResult verifyTensorReshapeOp(TensorReshapeOp op,
                                           RankedTensorType expandedType,
                                           RankedTensorType collapsedType) {
  if (failed(
          verifyReshapeLikeTypes(op, expandedType, collapsedType, isExpansion)))
    return failure();

  auto maps = op.getReassociationMaps();
  RankedTensorType expectedType =
      computeTensorReshapeCollapsedType(expandedType, maps);
  if (collapsedType != expectedType)
    return op.emitOpError("expected collapsed type to be ")
           << expectedType << ", but got " << collapsedType;
  return success();
}

static LogicalResult verify(ExpandShapeOp op) {
  return verifyTensorReshapeOp(op, op.getResultType(), op.getSrcType());
}

static LogicalResult verify(CollapseShapeOp op) {
  return verifyTensorReshapeOp(op, op.getSrcType(), op.getResultType());
}

namespace {
/// Reshape of a splat constant can be replaced with a constant of the result
/// type.
template <typename TensorReshapeOp>
struct FoldReshapeWithConstant : OpRewritePattern<TensorReshapeOp> {
  using OpRewritePattern<TensorReshapeOp>::OpRewritePattern;
  LogicalResult matchAndRewrite(TensorReshapeOp reshapeOp,
                                PatternRewriter &rewriter) const override {
    DenseElementsAttr attr;
    if (!matchPattern(reshapeOp.src(), m_Constant(&attr)))
      return failure();
    if (!attr || !attr.isSplat())
      return failure();
    DenseElementsAttr newAttr = DenseElementsAttr::getFromRawBuffer(
        reshapeOp.getResultType(), attr.getRawData(), true);
    rewriter.replaceOpWithNewOp<arith::ConstantOp>(reshapeOp, newAttr);
    return success();
  }
};

} // namespace

void ExpandShapeOp::getCanonicalizationPatterns(RewritePatternSet &results,
                                                MLIRContext *context) {
  results.add<CollapseReshapeOps<ExpandShapeOp>,
              CollapseMixedReshapeOps<ExpandShapeOp, CollapseShapeOp>,
              FoldReshapeWithConstant<ExpandShapeOp>>(context);
}

void CollapseShapeOp::getCanonicalizationPatterns(RewritePatternSet &results,
                                                  MLIRContext *context) {
  results.add<CollapseReshapeOps<CollapseShapeOp>,
              CollapseMixedReshapeOps<CollapseShapeOp, ExpandShapeOp>,
              FoldReshapeWithConstant<CollapseShapeOp>>(context);
}

OpFoldResult ExpandShapeOp::fold(ArrayRef<Attribute> operands) {
  return foldReshapeOp<ExpandShapeOp, CollapseShapeOp>(*this, operands);
}
OpFoldResult CollapseShapeOp::fold(ArrayRef<Attribute> operands) {
  return foldReshapeOp<CollapseShapeOp, ExpandShapeOp>(*this, operands);
}

//===----------------------------------------------------------------------===//
// ExtractSliceOp
//===----------------------------------------------------------------------===//

/// An extract_slice op result type can be fully inferred from the source type
/// and the static representation of offsets, sizes and strides. Special
/// sentinels encode the dynamic case.
RankedTensorType
ExtractSliceOp::inferResultType(RankedTensorType sourceRankedTensorType,
                                ArrayRef<int64_t> leadingStaticOffsets,
                                ArrayRef<int64_t> leadingStaticSizes,
                                ArrayRef<int64_t> leadingStaticStrides) {
  // An extract_slice op may specify only a leading subset of offset/sizes/
  // strides in which case we complete with offset=0, sizes from memref type and
  // strides=1.
  unsigned rank = sourceRankedTensorType.getRank();
  assert(leadingStaticSizes.size() <= rank &&
         "unexpected leadingStaticSizes overflow");
  auto staticSizes = llvm::to_vector<4>(leadingStaticSizes);
  unsigned numTrailingSizes = rank - staticSizes.size();
  llvm::append_range(staticSizes, sourceRankedTensorType.getShape().take_back(
                                      numTrailingSizes));
  return RankedTensorType::get(staticSizes,
                               sourceRankedTensorType.getElementType());
}

RankedTensorType
ExtractSliceOp::inferResultType(RankedTensorType sourceRankedTensorType,
                                ArrayRef<OpFoldResult> leadingStaticOffsets,
                                ArrayRef<OpFoldResult> leadingStaticSizes,
                                ArrayRef<OpFoldResult> leadingStaticStrides) {
  SmallVector<int64_t> staticOffsets, staticSizes, staticStrides;
  SmallVector<Value> dynamicOffsets, dynamicSizes, dynamicStrides;
  dispatchIndexOpFoldResults(leadingStaticOffsets, dynamicOffsets,
                             staticOffsets, ShapedType::kDynamicStrideOrOffset);
  dispatchIndexOpFoldResults(leadingStaticSizes, dynamicSizes, staticSizes,
                             ShapedType::kDynamicSize);
  dispatchIndexOpFoldResults(leadingStaticStrides, dynamicStrides,
                             staticStrides, ShapedType::kDynamicStrideOrOffset);
  return ExtractSliceOp::inferResultType(sourceRankedTensorType, staticOffsets,
                                         staticSizes, staticStrides);
}

/// An extract_slice op result type can be fully inferred from the source type
/// and the static representation of offsets, sizes and strides. Special
/// sentinels encode the dynamic case.
RankedTensorType ExtractSliceOp::inferRankReducedResultType(
    unsigned resultRank, RankedTensorType sourceRankedTensorType,
    ArrayRef<int64_t> leadingStaticOffsets,
    ArrayRef<int64_t> leadingStaticSizes,
    ArrayRef<int64_t> leadingStaticStrides) {
  auto inferredType =
      inferResultType(sourceRankedTensorType, leadingStaticOffsets,
                      leadingStaticSizes, leadingStaticStrides)
          .cast<RankedTensorType>();
  int rankDiff = inferredType.getRank() - resultRank;
  if (rankDiff > 0) {
    auto shape = inferredType.getShape();
    llvm::SmallDenseSet<unsigned> dimsToProject;
    mlir::getPositionsOfShapeOne(rankDiff, shape, dimsToProject);
    SmallVector<int64_t> projectedShape;
    for (unsigned pos = 0, e = shape.size(); pos < e; ++pos)
      if (!dimsToProject.contains(pos))
        projectedShape.push_back(shape[pos]);
    inferredType =
        RankedTensorType::get(projectedShape, inferredType.getElementType());
  }
  return inferredType;
}

RankedTensorType ExtractSliceOp::inferRankReducedResultType(
    unsigned resultRank, RankedTensorType sourceRankedTensorType,
    ArrayRef<OpFoldResult> leadingStaticOffsets,
    ArrayRef<OpFoldResult> leadingStaticSizes,
    ArrayRef<OpFoldResult> leadingStaticStrides) {
  SmallVector<int64_t> staticOffsets, staticSizes, staticStrides;
  SmallVector<Value> dynamicOffsets, dynamicSizes, dynamicStrides;
  dispatchIndexOpFoldResults(leadingStaticOffsets, dynamicOffsets,
                             staticOffsets, ShapedType::kDynamicStrideOrOffset);
  dispatchIndexOpFoldResults(leadingStaticSizes, dynamicSizes, staticSizes,
                             ShapedType::kDynamicSize);
  dispatchIndexOpFoldResults(leadingStaticStrides, dynamicStrides,
                             staticStrides, ShapedType::kDynamicStrideOrOffset);
  return ExtractSliceOp::inferRankReducedResultType(
      resultRank, sourceRankedTensorType, staticOffsets, staticSizes,
      staticStrides);
}

/// Build an ExtractSliceOp with mixed static and dynamic entries and custom
/// result type. If the type passed is nullptr, it is inferred.
void ExtractSliceOp::build(OpBuilder &b, OperationState &result,
                           RankedTensorType resultType, Value source,
                           ArrayRef<OpFoldResult> offsets,
                           ArrayRef<OpFoldResult> sizes,
                           ArrayRef<OpFoldResult> strides,
                           ArrayRef<NamedAttribute> attrs) {
  SmallVector<int64_t> staticOffsets, staticSizes, staticStrides;
  SmallVector<Value> dynamicOffsets, dynamicSizes, dynamicStrides;
  dispatchIndexOpFoldResults(offsets, dynamicOffsets, staticOffsets,

                             ShapedType::kDynamicStrideOrOffset);
  dispatchIndexOpFoldResults(sizes, dynamicSizes, staticSizes,
                             ShapedType::kDynamicSize);
  dispatchIndexOpFoldResults(strides, dynamicStrides, staticStrides,

                             ShapedType::kDynamicStrideOrOffset);
  auto sourceRankedTensorType = source.getType().cast<RankedTensorType>();
  // Structuring implementation this way avoids duplication between builders.
  if (!resultType) {
    resultType =
        ExtractSliceOp::inferResultType(sourceRankedTensorType, staticOffsets,
                                        staticSizes, staticStrides)
            .cast<RankedTensorType>();
  }
  build(b, result, resultType, source, dynamicOffsets, dynamicSizes,
        dynamicStrides, b.getI64ArrayAttr(staticOffsets),
        b.getI64ArrayAttr(staticSizes), b.getI64ArrayAttr(staticStrides));
  result.addAttributes(attrs);
}

/// Build an ExtractSliceOp with mixed static and dynamic entries and inferred
/// result type.
void ExtractSliceOp::build(OpBuilder &b, OperationState &result, Value source,
                           ArrayRef<OpFoldResult> offsets,
                           ArrayRef<OpFoldResult> sizes,
                           ArrayRef<OpFoldResult> strides,
                           ArrayRef<NamedAttribute> attrs) {
  build(b, result, RankedTensorType(), source, offsets, sizes, strides, attrs);
}

/// Build an ExtractSliceOp with dynamic entries and custom result type. If the
/// type passed is nullptr, it is inferred.
void ExtractSliceOp::build(OpBuilder &b, OperationState &result,
                           RankedTensorType resultType, Value source,
                           ValueRange offsets, ValueRange sizes,
                           ValueRange strides, ArrayRef<NamedAttribute> attrs) {
  SmallVector<OpFoldResult> offsetValues = llvm::to_vector<4>(
      llvm::map_range(offsets, [](Value v) -> OpFoldResult { return v; }));
  SmallVector<OpFoldResult> sizeValues = llvm::to_vector<4>(
      llvm::map_range(sizes, [](Value v) -> OpFoldResult { return v; }));
  SmallVector<OpFoldResult> strideValues = llvm::to_vector<4>(
      llvm::map_range(strides, [](Value v) -> OpFoldResult { return v; }));
  build(b, result, resultType, source, offsetValues, sizeValues, strideValues);
}

/// Build an ExtractSliceOp with dynamic entries and inferred result type.
void ExtractSliceOp::build(OpBuilder &b, OperationState &result, Value source,
                           ValueRange offsets, ValueRange sizes,
                           ValueRange strides, ArrayRef<NamedAttribute> attrs) {
  build(b, result, RankedTensorType(), source, offsets, sizes, strides, attrs);
}

template <typename OpTy>
static LogicalResult produceSliceErrorMsg(SliceVerificationResult result,
                                          OpTy op, Type expectedType) {
  auto memrefType = expectedType.cast<ShapedType>();
  switch (result) {
  case SliceVerificationResult::Success:
    return success();
  case SliceVerificationResult::RankTooLarge:
    return op.emitError("expected rank to be smaller or equal to ")
           << "the other rank. ";
  case SliceVerificationResult::SizeMismatch:
    return op.emitError("expected type to be ")
           << expectedType << " or a rank-reduced version. (size mismatch) ";
  case SliceVerificationResult::ElemTypeMismatch:
    return op.emitError("expected element type to be ")
           << memrefType.getElementType();
  default:
    llvm_unreachable("unexpected extract_slice op verification result");
  }
}

/// Verifier for ExtractSliceOp.
static LogicalResult verify(ExtractSliceOp op) {
  // Verify result type against inferred type.
  auto expectedType =
      ExtractSliceOp::inferResultType(op.getSourceType(), op.getMixedOffsets(),
                                      op.getMixedSizes(), op.getMixedStrides());
  auto result =
      isRankReducedType(expectedType.cast<ShapedType>(), op.getType());
  return produceSliceErrorMsg(result, op, expectedType);
}

/// Infer the canonical type of the result of an extract_slice op. Returns a
/// type with rank `resultRank` that is either the rank of the rank-reduced
/// type, or the non-rank-reduced type.
static RankedTensorType
getCanonicalSliceResultType(unsigned resultRank, RankedTensorType sourceType,
                            ArrayRef<OpFoldResult> mixedOffsets,
                            ArrayRef<OpFoldResult> mixedSizes,
                            ArrayRef<OpFoldResult> mixedStrides) {
  auto resultType =
      ExtractSliceOp::inferRankReducedResultType(
          resultRank, sourceType, mixedOffsets, mixedSizes, mixedStrides)
          .cast<RankedTensorType>();
  if (resultType.getRank() != resultRank) {
    resultType = ExtractSliceOp::inferResultType(sourceType, mixedOffsets,
                                                 mixedSizes, mixedStrides)
                     .cast<RankedTensorType>();
  }
  return resultType;
}

llvm::SmallDenseSet<unsigned> ExtractSliceOp::getDroppedDims() {
  llvm::SmallDenseSet<unsigned> droppedDims;
  ArrayRef<int64_t> resultShape = getType().getShape();
  SmallVector<OpFoldResult> mixedSizes = getMixedSizes();
  unsigned shapePos = 0;
  for (auto size : enumerate(mixedSizes)) {
    Optional<int64_t> sizeVal = getConstantIntValue(size.value());
    // If the size is not 1, or if the current matched dimension of the result
    // is the same static shape as the size value (which is 1), then the
    // dimension is preserved.
    if (!sizeVal || sizeVal.getValue() != 1 ||
        (shapePos < resultShape.size() && resultShape[shapePos] == 1)) {
      shapePos++;
      continue;
    }
    droppedDims.insert(size.index());
  }
  return droppedDims;
}

LogicalResult ExtractSliceOp::reifyResultShapes(
    OpBuilder &builder, ReifiedRankedShapedTypeDims &reifiedReturnShapes) {
  reifiedReturnShapes.resize(1);
  reifiedReturnShapes[0].reserve(getType().getRank());
  SmallVector<OpFoldResult> mixedSizes = getMixedSizes();
  llvm::SmallDenseSet<unsigned> droppedDims = getDroppedDims();
  Location loc = getLoc();
  for (auto size : enumerate(mixedSizes)) {
    if (droppedDims.count(size.index()))
      continue;
    if (auto attr = size.value().dyn_cast<Attribute>()) {
      reifiedReturnShapes[0].push_back(builder.create<arith::ConstantIndexOp>(
          loc, attr.cast<IntegerAttr>().getInt()));
      continue;
    }
    reifiedReturnShapes[0].push_back(size.value().get<Value>());
  }
  return success();
}

namespace {
/// Pattern to rewrite an extract_slice op with tensor::Cast arguments.
/// This essentially pushes memref_cast past its consuming slice when
/// `canFoldIntoConsumerOp` is true.
///
/// Example:
/// ```
///   %0 = tensor.cast %V : tensor<16x16xf32> to tensor<?x?xf32>
///   %1 = tensor.extract_slice %0[0, 0][3, 4][1, 1] : tensor<?x?xf32> to
///   tensor<3x4xf32>
/// ```
/// is rewritten into:
/// ```
///   %0 = tensor.extract_slice %V[0, 0][3, 4][1, 1] : tensor<16x16xf32> to
///   tensor<3x4xf32> %1 = tensor.cast %0: tensor<3x4xf32> to tensor<3x4xf32>
/// ```
class ExtractSliceOpCastFolder final : public OpRewritePattern<ExtractSliceOp> {
public:
  using OpRewritePattern<ExtractSliceOp>::OpRewritePattern;

  LogicalResult matchAndRewrite(ExtractSliceOp sliceOp,
                                PatternRewriter &rewriter) const override {
    // Any constant operand, just return to let SubViewOpConstantFolder kick in.
    if (llvm::any_of(sliceOp.getOperands(), [](Value operand) {
          return matchPattern(operand, matchConstantIndex());
        }))
      return failure();

    auto castOp = sliceOp.source().getDefiningOp<tensor::CastOp>();
    if (!castOp)
      return failure();

    if (!canFoldIntoConsumerOp(castOp))
      return failure();

    /// Deduce the type of the result to use for the canonicalized operation.
    RankedTensorType resultType = getCanonicalSliceResultType(
        sliceOp.getType().getRank(), sliceOp.getSourceType(),
        sliceOp.getMixedOffsets(), sliceOp.getMixedSizes(),
        sliceOp.getMixedStrides());
    Value newSlice = rewriter.create<ExtractSliceOp>(
        sliceOp.getLoc(), resultType, castOp.source(), sliceOp.offsets(),
        sliceOp.sizes(), sliceOp.strides(), sliceOp.static_offsets(),
        sliceOp.static_sizes(), sliceOp.static_strides());
    rewriter.replaceOpWithNewOp<tensor::CastOp>(sliceOp, sliceOp.getType(),
                                                newSlice);
    return success();
  }
};
} // namespace

/// Return the canonical type of the result of an extract_slice op.
struct SliceReturnTypeCanonicalizer {
  RankedTensorType operator()(ExtractSliceOp op,
                              ArrayRef<OpFoldResult> mixedOffsets,
                              ArrayRef<OpFoldResult> mixedSizes,
                              ArrayRef<OpFoldResult> mixedStrides) {
    return getCanonicalSliceResultType(op.getType().getRank(),
                                       op.getSourceType(), mixedOffsets,
                                       mixedSizes, mixedStrides);
  }
};

/// A canonicalizer wrapper to replace ExtractSliceOps.
struct SliceCanonicalizer {
  void operator()(PatternRewriter &rewriter, ExtractSliceOp op,
                  ExtractSliceOp newOp) {
    Value replacement = newOp.getResult();
    if (replacement.getType() != op.getType())
      replacement = rewriter.create<tensor::CastOp>(op.getLoc(), op.getType(),
                                                    replacement);
    rewriter.replaceOp(op, replacement);
  }
};

void ExtractSliceOp::getCanonicalizationPatterns(RewritePatternSet &results,
                                                 MLIRContext *context) {
  results.add<
      OpWithOffsetSizesAndStridesConstantArgumentFolder<
          ExtractSliceOp, SliceReturnTypeCanonicalizer, SliceCanonicalizer>,
      ExtractSliceOpCastFolder>(context);
}

//
static LogicalResult
foldIdentityOffsetSizeAndStrideOpInterface(OffsetSizeAndStrideOpInterface op,
                                           ShapedType shapedType) {
  OpBuilder b(op.getContext());
  for (OpFoldResult ofr : op.getMixedOffsets())
    if (getConstantIntValue(ofr) != static_cast<int64_t>(0))
      return failure();
  // Rank-reducing noops only need to inspect the leading dimensions: llvm::zip
  // is appropriate.
  auto shape = shapedType.getShape();
  for (auto it : llvm::zip(op.getMixedSizes(), shape))
    if (getConstantIntValue(std::get<0>(it)) != std::get<1>(it))
      return failure();
  for (OpFoldResult ofr : op.getMixedStrides())
    if (getConstantIntValue(ofr) != static_cast<int64_t>(1))
      return failure();
  return success();
}

/// If we have an ExtractSliceOp consuming an InsertSliceOp with the same slice,
/// we can return the InsertSliceOp's source directly.
// TODO: This only checks the immediate producer; extend to go up the
// insert/extract chain if the slices are disjoint.
static Value foldExtractAfterInsertSlice(ExtractSliceOp extractOp) {
  auto insertOp = extractOp.source().getDefiningOp<InsertSliceOp>();

  auto isSame = [](OpFoldResult a, OpFoldResult b) { return a == b; };
  if (insertOp && insertOp.source().getType() == extractOp.getType() &&
      insertOp.isSameAs(extractOp, isSame))
    return insertOp.source();

  return {};
}

OpFoldResult ExtractSliceOp::fold(ArrayRef<Attribute>) {
  if (getSourceType() == getType() &&
      succeeded(foldIdentityOffsetSizeAndStrideOpInterface(*this, getType())))
    return this->source();
  if (Value slice = foldExtractAfterInsertSlice(*this))
    return slice;
  return OpFoldResult();
}

Value mlir::tensor::createCanonicalRankReducingExtractSliceOp(
    OpBuilder &b, Location loc, Value tensor, RankedTensorType targetType) {
  auto rankedTensorType = tensor.getType().cast<RankedTensorType>();
  unsigned rank = rankedTensorType.getRank();
  auto shape = rankedTensorType.getShape();
  SmallVector<OpFoldResult> offsets(rank, b.getIndexAttr(0));
  SmallVector<OpFoldResult> sizes;
  for (unsigned i = 0, e = rank; i < e; ++i) {
    OpFoldResult dim;
    if (rankedTensorType.isDynamicDim(i))
      dim = b.createOrFold<tensor::DimOp>(
          loc, tensor, b.create<arith::ConstantIndexOp>(loc, i));
    else
      dim = b.getIndexAttr(shape[i]);
    sizes.push_back(dim);
  }
  SmallVector<OpFoldResult> strides(rank, b.getIndexAttr(1));
  return b.createOrFold<tensor::ExtractSliceOp>(loc, targetType, tensor,
                                                offsets, sizes, strides);
}

//===----------------------------------------------------------------------===//
// InsertSliceOp
//===----------------------------------------------------------------------===//

// Build a InsertSliceOp with mixed static and dynamic entries.
void InsertSliceOp::build(OpBuilder &b, OperationState &result, Value source,
                          Value dest, ArrayRef<OpFoldResult> offsets,
                          ArrayRef<OpFoldResult> sizes,
                          ArrayRef<OpFoldResult> strides,
                          ArrayRef<NamedAttribute> attrs) {
  SmallVector<int64_t> staticOffsets, staticSizes, staticStrides;
  SmallVector<Value> dynamicOffsets, dynamicSizes, dynamicStrides;
  dispatchIndexOpFoldResults(offsets, dynamicOffsets, staticOffsets,

                             ShapedType::kDynamicStrideOrOffset);
  dispatchIndexOpFoldResults(sizes, dynamicSizes, staticSizes,
                             ShapedType::kDynamicSize);
  dispatchIndexOpFoldResults(strides, dynamicStrides, staticStrides,

                             ShapedType::kDynamicStrideOrOffset);
  build(b, result, dest.getType(), source, dest, dynamicOffsets, dynamicSizes,
        dynamicStrides, b.getI64ArrayAttr(staticOffsets),
        b.getI64ArrayAttr(staticSizes), b.getI64ArrayAttr(staticStrides));
  result.addAttributes(attrs);
}

// Build a InsertSliceOp with dynamic entries.
void InsertSliceOp::build(OpBuilder &b, OperationState &result, Value source,
                          Value dest, ValueRange offsets, ValueRange sizes,
                          ValueRange strides, ArrayRef<NamedAttribute> attrs) {
  SmallVector<OpFoldResult> offsetValues = llvm::to_vector<4>(
      llvm::map_range(offsets, [](Value v) -> OpFoldResult { return v; }));
  SmallVector<OpFoldResult> sizeValues = llvm::to_vector<4>(
      llvm::map_range(sizes, [](Value v) -> OpFoldResult { return v; }));
  SmallVector<OpFoldResult> strideValues = llvm::to_vector<4>(
      llvm::map_range(strides, [](Value v) -> OpFoldResult { return v; }));
  build(b, result, source, dest, offsetValues, sizeValues, strideValues);
}

/// Verifier for InsertSliceOp.
static LogicalResult verify(InsertSliceOp op) {
  // insert_slice is the inverse of extract_slice, use the same type inference.
  auto expectedType = ExtractSliceOp::inferRankReducedResultType(
      op.getSourceType().getRank(), op.getType(),
      extractFromI64ArrayAttr(op.static_offsets()),
      extractFromI64ArrayAttr(op.static_sizes()),
      extractFromI64ArrayAttr(op.static_strides()));
  auto result =
      isRankReducedType(expectedType.cast<ShapedType>(), op.getSourceType());
  return produceSliceErrorMsg(result, op, expectedType);
}

/// If we have two consecutive InsertSliceOp writing to the same slice, we
/// can mutate the second InsertSliceOp's destination to the first one's.
///
/// Example:
///
/// ```mlir
///   %0 = tensor.insert_slice %slice0 into %input[0, 0] [64, 64] [1, 1]
///   %1 = tensor.insert_slice %slice1 into %0[0, 0] [64, 64] [1, 1]
/// ```
///
/// folds into:
///
/// ```mlir
///   %1 = tensor.insert_slice %slice1 into %input[0, 0] [64, 64] [1, 1]
/// ```
static LogicalResult foldInsertAfterInsertSlice(InsertSliceOp insertOp) {
  auto prevInsertOp = insertOp.dest().getDefiningOp<InsertSliceOp>();

  auto isSame = [](OpFoldResult a, OpFoldResult b) { return a == b; };
  if (!prevInsertOp ||
      prevInsertOp.source().getType() != insertOp.source().getType() ||
      !prevInsertOp.isSameAs(insertOp, isSame))
    return failure();

  insertOp.destMutable().assign(prevInsertOp.dest());
  return success();
}

OpFoldResult InsertSliceOp::fold(ArrayRef<Attribute>) {
  if (getSourceType().hasStaticShape() && getType().hasStaticShape() &&
      getSourceType() == getType() &&
      succeeded(foldIdentityOffsetSizeAndStrideOpInterface(*this, getType())))
    return this->source();
  if (succeeded(foldInsertAfterInsertSlice(*this)))
    return getResult();
  return OpFoldResult();
}

LogicalResult InsertSliceOp::reifyResultShapes(
    OpBuilder &builder, ReifiedRankedShapedTypeDims &reifiedReturnShapes) {
  reifiedReturnShapes.resize(1, SmallVector<Value>(getType().getRank()));
  for (auto dim : llvm::seq<int64_t>(0, getType().getRank())) {
    reifiedReturnShapes[0][dim] =
        builder.createOrFold<tensor::DimOp>(getLoc(), dest(), dim);
  }
  return success();
}

namespace {
/// Pattern to rewrite a insert_slice op with constant arguments.
class InsertSliceOpConstantArgumentFolder final
    : public OpRewritePattern<InsertSliceOp> {
public:
  using OpRewritePattern<InsertSliceOp>::OpRewritePattern;

  LogicalResult matchAndRewrite(InsertSliceOp insertSliceOp,
                                PatternRewriter &rewriter) const override {
    // No constant operand, just return.
    if (llvm::none_of(insertSliceOp.getOperands(), [](Value operand) {
          return matchPattern(operand, matchConstantIndex());
        }))
      return failure();

    // At least one of offsets/sizes/strides is a new constant.
    // Form the new list of operands and constant attributes from the
    // existing.
    SmallVector<OpFoldResult> mixedOffsets(insertSliceOp.getMixedOffsets());
    SmallVector<OpFoldResult> mixedSizes(insertSliceOp.getMixedSizes());
    SmallVector<OpFoldResult> mixedStrides(insertSliceOp.getMixedStrides());
    canonicalizeSubViewPart(mixedOffsets, ShapedType::isDynamicStrideOrOffset);
    canonicalizeSubViewPart(mixedSizes, ShapedType::isDynamic);
    canonicalizeSubViewPart(mixedStrides, ShapedType::isDynamicStrideOrOffset);

    // Create the new op in canonical form.
    auto sourceType = ExtractSliceOp::inferRankReducedResultType(
        insertSliceOp.getSourceType().getRank(), insertSliceOp.getType(),
        mixedOffsets, mixedSizes, mixedStrides);
    Value toInsert = insertSliceOp.source();
    if (sourceType != insertSliceOp.getSourceType())
      toInsert = rewriter.create<tensor::CastOp>(insertSliceOp.getLoc(),
                                                 sourceType, toInsert);
    rewriter.replaceOpWithNewOp<InsertSliceOp>(
        insertSliceOp, toInsert, insertSliceOp.dest(), mixedOffsets, mixedSizes,
        mixedStrides);
    return success();
  }
};

/// Fold tensor_casts with insert_slice operations. If the source or destination
/// tensor is a tensor_cast that removes static type information, the cast is
/// folded into the insert_slice operation. E.g.:
///
/// ```mlir
///   %1 = tensor.cast %0 : tensor<8x16xf32> to tensor<?x?xf32>
///   %2 = tensor.insert_slice %1 into ... : tensor<?x?xf32> into ...
/// ```
///
/// folds into:
///
/// ```mlir
///   %2 = tensor.insert_slice %0 into ... : tensor<8x16xf32> into ...
/// ```
///
/// Note: When folding a cast on the destination tensor, the result of the
/// insert_slice operation is casted to ensure that the type of the result did
/// not change.
struct InsertSliceOpCastFolder final : public OpRewritePattern<InsertSliceOp> {
  using OpRewritePattern<InsertSliceOp>::OpRewritePattern;

  LogicalResult matchAndRewrite(InsertSliceOp insertSliceOp,
                                PatternRewriter &rewriter) const override {
    if (llvm::any_of(insertSliceOp.getOperands(), [](Value operand) {
          return matchPattern(operand, matchConstantIndex());
        }))
      return failure();

    auto getSourceOfCastOp = [](Value v) -> Optional<Value> {
      auto castOp = v.getDefiningOp<tensor::CastOp>();
      if (!castOp || !canFoldIntoConsumerOp(castOp))
        return llvm::None;
      return castOp.source();
    };
    Optional<Value> sourceCastSource =
        getSourceOfCastOp(insertSliceOp.source());
    Optional<Value> destCastSource = getSourceOfCastOp(insertSliceOp.dest());
    if (!sourceCastSource && !destCastSource)
      return failure();

    Value replacement = rewriter.create<InsertSliceOp>(
        insertSliceOp.getLoc(),
        (sourceCastSource ? *sourceCastSource : insertSliceOp.source()),
        (destCastSource ? *destCastSource : insertSliceOp.dest()),
        insertSliceOp.getMixedOffsets(), insertSliceOp.getMixedSizes(),
        insertSliceOp.getMixedStrides());

    if (replacement.getType() != insertSliceOp.getType()) {
      replacement = rewriter.create<tensor::CastOp>(
          insertSliceOp.getLoc(), insertSliceOp.getType(), replacement);
    }
    rewriter.replaceOp(insertSliceOp, replacement);
    return success();
  }
};

/// If additional static type information can be deduced from a insert_slice's
/// size operands, insert an explicit cast of the op's source operand. This
/// enables other canonicalization patterns that are matching for tensor_cast
/// ops such as `ForOpTensorCastFolder` in SCF.
///
/// Example:
///
/// ```mlir
///   %r = tensor.insert_slice %0 into %1[...] [64, 64] [1, 1]
///       : tensor<?x?xf32> into ...
/// ```
///
/// folds into:
///
/// ```mlir
///   %tmp = tensor.cast %0 : tensor<?x?xf32> to tensor<64x64xf32>
///   %r = tensor.insert_slice %tmp into %1[...] [64, 64] [1, 1]
///       : tensor<64x64xf32> into ...
/// ```
struct InsertSliceOpSourceCastInserter final
    : public OpRewritePattern<InsertSliceOp> {
  using OpRewritePattern<InsertSliceOp>::OpRewritePattern;

  LogicalResult matchAndRewrite(InsertSliceOp insertSliceOp,
                                PatternRewriter &rewriter) const override {
    RankedTensorType srcType = insertSliceOp.getSourceType();
    if (srcType.getRank() != insertSliceOp.getType().getRank())
      return failure();
    SmallVector<int64_t> newSrcShape(srcType.getShape().begin(),
                                     srcType.getShape().end());
    for (int64_t i = 0; i < srcType.getRank(); ++i) {
      if (Optional<int64_t> constInt =
              getConstantIntValue(insertSliceOp.getMixedSizes()[i]))
        newSrcShape[i] = *constInt;
    }

    RankedTensorType newSrcType =
        RankedTensorType::get(newSrcShape, srcType.getElementType());
    if (srcType == newSrcType ||
        !preservesStaticInformation(srcType, newSrcType) ||
        !tensor::CastOp::areCastCompatible(srcType, newSrcType))
      return failure();

    // newSrcType is:
    //   1) Different from srcType.
    //   2) "More static" than srcType.
    //   3) Cast-compatible with srcType.
    // Insert the cast.
    Value cast = rewriter.create<tensor::CastOp>(
        insertSliceOp.getLoc(), newSrcType, insertSliceOp.source());
    rewriter.replaceOpWithNewOp<InsertSliceOp>(
        insertSliceOp, cast, insertSliceOp.dest(),
        insertSliceOp.getMixedOffsets(), insertSliceOp.getMixedSizes(),
        insertSliceOp.getMixedStrides());
    return success();
  }
};
} // namespace

void InsertSliceOp::getCanonicalizationPatterns(RewritePatternSet &results,
                                                MLIRContext *context) {
  results.add<InsertSliceOpConstantArgumentFolder, InsertSliceOpCastFolder,
              InsertSliceOpSourceCastInserter>(context);
}

Value mlir::tensor::createCanonicalRankReducingInsertSliceOp(OpBuilder &b,
                                                             Location loc,
                                                             Value tensor,
                                                             Value dest) {
  auto rankedTensorType = dest.getType().cast<RankedTensorType>();
  unsigned rank = rankedTensorType.getRank();
  auto shape = rankedTensorType.getShape();
  SmallVector<OpFoldResult> offsets(rank, b.getIndexAttr(0));
  SmallVector<OpFoldResult> sizes;
  for (unsigned i = 0, e = rank; i < e; ++i) {
    OpFoldResult dim;
    if (rankedTensorType.isDynamicDim(i))
      dim = b.createOrFold<tensor::DimOp>(
          loc, dest, b.create<arith::ConstantIndexOp>(loc, i));
    else
      dim = b.getIndexAttr(shape[i]);
    sizes.push_back(dim);
  }
  SmallVector<OpFoldResult> strides(rank, b.getIndexAttr(1));
  return b.createOrFold<tensor::InsertSliceOp>(loc, tensor, dest, offsets,
                                               sizes, strides);
}

//===----------------------------------------------------------------------===//
// TableGen'd op method definitions
//===----------------------------------------------------------------------===//

#define GET_OP_CLASSES
#include "mlir/Dialect/Tensor/IR/TensorOps.cpp.inc"