#include "mlir/Dialect/Arith/IR/Arith.h"  // from @llvm-project
#include "mlir/Dialect/Func/IR/FuncOps.h"  // from @llvm-project
#include "mlir/Dialect/Shape/IR/Shape.h"  // from @llvm-project
#include "mlir/Dialect/Tensor/IR/Tensor.h"  // from @llvm-project
#include "mlir/Transforms/DialectConversion.h"  // from @llvm-project
#include "stablehlo/dialect/ChloOps.h"  // from @stablehlo

namespace tf_executor {

class TensorFlowExecutorDialect : public Dialect {
 public:
  static StringRef getDialectNamespace() { return "tf_executor"; }
  explicit TensorFlowExecutorDialect(MLIRContext *context);

  // Parses a type registered to this dialect.
  Type parseType(DialectAsmParser &parser) const override;

  // Prints a type registered to this dialect.
  void printType(Type type, DialectAsmPrinter &os) const override;
};

namespace tensorflow {
namespace tf2xla {
namespace v2 {

// Given the input Module op that's in the Tensorflow Dialect, convert the MLIR
// module in place to the Tensorflow Executor Dialect. Returns an OK Status if
// success, otherwise failure with an error message.
// The Tensorflow Executor Dialect is required to export an MLIR module to a
// Tensorflow GraphDef. This API will add control dependencies and verify that

struct TFReductionPatternInterface
    : public mlir::DialectReductionPatternInterface {
 public:
  explicit TFReductionPatternInterface(mlir::Dialect *dialect)
      : DialectReductionPatternInterface(dialect) {}

  void populateReductionPatterns(
      mlir::RewritePatternSet &patterns) const final {
    populateWithGenerated(patterns);
  }
};

}  // namespace

                       bool would_be_cloned,
                       mlir::IRMapping &mapping) const final {
    // All mlrt dialect ops can be inlined.
    return true;
  }
};

}  // namespace

MlrtDialect::MlrtDialect(mlir::MLIRContext *context)
    : mlir::Dialect(/*name=*/"mlrt", context,
                    mlir::TypeID::get<MlrtDialect>()) {
  addTypes<FutureType>();
  addTypes<PromiseType>();

    bool prefer_tf2xla = false);

// Legalizes TF/XLA communication ops (TF dialect) to HLO dialect communication
// ops.
std::unique_ptr<OperationPass<ModuleOp>> CreateLegalizeTFCommunicationPass();

// Legalizes TF/XLA collective ops (TF dialect) to HLO dialect collective
// ops.
std::unique_ptr<OperationPass<ModuleOp>> CreateLegalizeTFCollectivePass();

even the Swift or Clang type systems (which are built around Swift/Clang
declaration nodes) in the future.

If you want to connect a new low-level compiler, you would create a new dialect
and the lowerings between the TensorFlow Graph dialect and your dialect.
This smooths the path for hardware and compiler makers. You can even target
dialects at different levels in the same model; the higher-level optimizers

            """ + buildFile.text

            file("direct/build.gradle") << """
                plugins {
                    id 'java-library'
                    id 'jacoco'
                }

                dependencies {
                    testImplementation project(":transitive")
                }
            """
            file("direct/src/main/java/direct/Multiplier.java").java """
                package direct;

#include "tensorflow/compiler/mlir/register_common_dialects.h"

#include "mlir/Dialect/Quant/QuantOps.h"  // from @llvm-project
#include "mlir/Dialect/Shape/IR/Shape.h"  // from @llvm-project
#include "mlir/Dialect/Tensor/IR/Tensor.h"  // from @llvm-project
#include "mlir/Dialect/Tosa/IR/TosaOps.h"  // from @llvm-project
#include "mlir/InitAllDialects.h"  // from @llvm-project

  print_flags.elideLargeElementsAttrs();

  if (VLOG_IS_ON(1)) {
    VLOG(1) << "Input TF Executor dialect:";
    DumpMlirOpToFile("tf_to_tfrt_tf_executor_dialect", module);
  }

  mlir::StatusScopedDiagnosticHandler diag_handler(module.getContext());

  // Lower MLIR TF Dialect to MLIR TFRT CoreRT dialect.
  mlir::PassManager pm(module.getContext());
  tensorflow::applyTensorflowAndCLOptions(pm);