// `from` to the returned set. This is used to partition patterns by op so they
// can be cleanly migrated from the old bridge to the MLIR bridge.
RewritePatternSet PatternsIncludeOps(RewritePatternSet &from) {
  RewritePatternSet to(from.getContext());
  // Filter NativePatterns.
  for (auto &pattern : from.getNativePatterns()) {
    std::optional<OperationName> pat_op_name = pattern->getRootKind();

// minimum, names should clearly convey scope). In the mean time, for a real
// compiler, use PopulateTFLoweringBeforeHLOPatterns.
void PopulateLoweringTFPatterns(MLIRContext *context,
                                RewritePatternSet *patterns);

// Populates TensorFlow lowering patterns to lower some of the TensorFlow
// operations that can be represented by means of other TensorFlow operations.

// Adds the HLO to TF rewrite patterns to the specified pattern list.
void PopulateLegalizeHloToTfPatterns(RewritePatternSet* patterns,
                                     MLIRContext* context);

// Adds the HLO to TFLite rewrite patterns to the specified pattern list.
void PopulateLegalizeHloToTFLitePatterns(RewritePatternSet* patterns,
                                         MLIRContext* context);

#define GEN_PASS_DECL

namespace mlir {
namespace TFL {
namespace tac {

constexpr char ExampleHardware::kId[];  // Define kId.

mlir::RewritePatternSet ExampleHardware::GetTransformations(
    MLIRContext* context) const {
  mlir::RewritePatternSet patterns(context);

  patterns.add<LowerPackIntoConcatReshape, UnrollSplit, UnrollSplitV, PadSlice,
               PadConcat>(context);
  return patterns;
}

// Default values.
constexpr float kNNAPIDefaultFixedValuedCost = 10000.0;

constexpr char NNAPIHardware::kId[];  // Define kId.

mlir::RewritePatternSet NNAPIHardware::GetTransformations(
    MLIRContext* context) const {
  mlir::RewritePatternSet patterns(context);

  patterns.add<SquaredDifference, LowerPackIntoConcatReshape,
               ReduceMeanToAvgPool, InsertRequantForReduceMean>(context);

namespace mlir::odml {

// Populates the pattern set with all folding patterns. These patterns
// are intended to have precedence over any other patterns added to the set.
void PopulateFolderPatterns(RewritePatternSet &patternSet);

}  // namespace mlir::odml

};

void DeviceTransformGPUPass::runOnOperation() {
  auto func = getOperation();
  auto* ctx = &getContext();
  RewritePatternSet patterns = GetHardwareRewritePatternsGPU(ctx);
  (void)applyPatternsAndFoldGreedily(func, std::move(patterns));
}

}  // namespace

RewritePatternSet GetHardwareRewritePatternsGPU(MLIRContext* context) {
  GpuHardware gpu_hardware;
  return gpu_hardware.GetTransformations(context);
}

namespace mlir {
namespace TF {

// Populates patterns to perform optimizations specific to tf.data operations.
void PopulateTFDataOptimizationPatterns(MLIRContext *context,
                                        RewritePatternSet *patterns);

}  // namespace TF
}  // namespace mlir

// This is used by TAC to get op supported/ op cost estimates on GPU.
class GpuHardware : public TargetHardware {
 public:
  static constexpr char kId[] = "GPU";
  mlir::RewritePatternSet GetTransformations(
      MLIRContext* context) const override;

  mlir::TypeID GetTypeId() const override {
    return mlir::TypeID::get<GpuHardware>();
  }

  MLIR_DEFINE_EXPLICIT_INTERNAL_INLINE_TYPE_ID(IdentifyDilatedConvPass)
  void runOnOperation() override;
};

void IdentifyDilatedConvPass::runOnOperation() {
  RewritePatternSet patterns(&getContext());
  auto func = getOperation();

  patterns.add<ConvertTFDilatedConvOp<TF::Conv2DOp>,
               ConvertTFDilatedConvOp<TF::DepthwiseConv2dNativeOp>>(
      &getContext());