#include "mlir/Dialect/Arith/IR/Arith.h"  // from @llvm-project
#include "mlir/Dialect/Func/Extensions/AllExtensions.h"  // from @llvm-project
#include "mlir/Dialect/Func/IR/FuncOps.h"  // from @llvm-project
#include "mlir/Dialect/Quant/QuantOps.h"  // from @llvm-project
#include "mlir/Dialect/SCF/IR/SCF.h"  // from @llvm-project
#include "mlir/Dialect/Shape/IR/Shape.h"  // from @llvm-project

#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

    "Counts how often a successful export from TF Dialect to Executor Dialect "
    "is",
    "status");

constexpr char kExportSuccess[] = "success";
constexpr char kExportFailed[] = "failed";

namespace {

void AddTfDialectToExecutorPasses(OpPassManager &pm) {
  pm.addPass(mlir::TF::CreateTFRegionControlFlowToFunctional());

  // First, we need to convert from functional, to executor dialect.
  pm.addNestedPass<FuncOp>(

// Executor Dialect for graph nodes and edges or be in TF Functional already.
// Individual Op inside a node should be the Tensorflow Functional Dialect. The
// output MLIR is in the TF Functional Dialect. Returns OkStatus if passed,
// otherwise an error.
//
// Inputs:
//   module - The MLIR Module that will be clustered. Expected to be in TF
//   Executor Dialect or TF Functional Dialect. Will convert to TF Functional.

#include <string>
#include <utility>

#include "llvm/ADT/StringRef.h"
#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/IR/Dialect.h"  // from @llvm-project
#include "mlir/IR/PatternMatch.h"  // from @llvm-project

    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();

            "QuantOps.cc.inc",
        ),
        (
            [
                "-gen-dialect-decls",
                "-dialect=quantfork",
            ],
            "QuantOpsDialect.h.inc",
        ),
        (
            [
                "-gen-dialect-defs",
                "-dialect=quantfork",
            ],
            "QuantOpsDialect.cc.inc",
        ),
    ],

            "QuantOps.cc.inc",
        ),
        (
            [
                "-gen-dialect-decls",
                "-dialect=quantization",
            ],
            "QuantOpsDialect.h.inc",
        ),
        (
            [
                "-gen-dialect-defs",
                "-dialect=quantization",
            ],
            "QuantOpsDialect.cc.inc",
        ),
    ],

#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);