#include "tensorflow/compiler/mlir/tensorflow/ir/tf_executor.h"
#include "tensorflow/compiler/mlir/tensorflow/ir/tf_saved_model.h"
#include "tensorflow/compiler/mlir/tensorflow/transforms/passes.h"
#include "xla/mlir_hlo/mhlo/IR/register.h"
#include "xla/mlir_hlo/mhlo/transforms/passes.h"
#include "tensorflow/core/ir/types/dialect.h"

int main(int argc, char** argv) {
  tensorflow::InitMlir y(&argc, &argv);

  mlir::registerAllPasses();

#include "tensorflow/compiler/mlir/tensorflow/ir/tf_types.h"
#include "tensorflow/compiler/mlir/tensorflow/utils/device_util.h"
#include "tensorflow/compiler/mlir/utils/string_container_utils.h"
#include "xla/array4d.h"
#include "xla/service/computation_placer.h"
#include "xla/xla_data.pb.h"
#include "tensorflow/core/framework/types.h"
#include "tensorflow/core/protobuf/tpu/topology.pb.h"
#include "tensorflow/core/util/device_name_utils.h"

#include "tensorflow/compiler/tf2xla/layout_util.h"
#include "tensorflow/compiler/tf2xla/xla_helpers.h"
#include "xla/client/client_library.h"
#include "xla/shape.h"
#include "xla/stream_executor/platform_manager.h"
#include "xla/translate/mhlo_to_hlo/type_to_shape.h"
#include "tensorflow/core/framework/tensor_shape.h"
#include "tensorflow/core/lib/monitoring/cell_reader.h"

#include <ostream>

#include "tensorflow/compiler/tf2xla/tf2xla.pb.h"
#include "xla/client/xla_computation.h"
#include "tensorflow/core/platform/errors.h"
#include "tensorflow/core/platform/status.h"

namespace tensorflow {
namespace tfcompile {

using QuantizeXlaFn = std::function<Status(const tf2xla::Config& config,
                                           xla::XlaComputation* computation)>;

      llvm::cl::desc("Choose target opset."),
      llvm::cl::values(
          clEnumValN(OpSet::TF, "TF",
                     "Uses TF ops that mimic quantization behavior"),
          clEnumValN(OpSet::XLA, "XLA", "Uses TF XLA ops"),
          clEnumValN(OpSet::UNIFORM_QUANTIZED, "UNIFORM_QUANTIZED",
                     "Uses TF Uniform Quantized ops"))};

  // Initialize for tests.
  void initializeForTest() {

#include "tensorflow/compiler/mlir/tensorflow/ir/tf_ops.h"
#include "tensorflow/compiler/mlir/tf2xla/transforms/utils.h"
#include "xla/mlir_hlo/mhlo/IR/hlo_ops.h"
#include "xla/mlir_hlo/utils/convert_op_folder.h"
#include "xla/mlir_hlo/utils/hlo_utils.h"
#include "xla/xla_data.pb.h"

namespace mlir {
namespace mhlo {

namespace {

constexpr absl::string_view kGroupSizeAttrName =

  EXPECT_TRUE(
      ShapeUtil::Compatible(xla::Shape(muladd_shape->parameters(0)), f32_2x2));
  EXPECT_TRUE(
      ShapeUtil::Compatible(xla::Shape(muladd_shape->parameters(1)), f32_2x2));

  const xla::Shape muladd_result(muladd_shape->result());
  ASSERT_EQ(muladd_result.element_type(), xla::TUPLE);
  ASSERT_EQ(ShapeUtil::TupleElementCount(muladd_result), 2);
  const xla::Shape& muladd_result0 =

#include "tensorflow/compiler/jit/tests/device_compiler_test_helper.h"

#include <string>

#include "absl/strings/match.h"
#include "tensorflow/compiler/jit/xla_compilation_cache.pb.h"
#include "xla/service/hlo.pb.h"
#include "tensorflow/core/platform/path.h"
#include "tensorflow/core/public/session.h"

namespace tensorflow {
namespace {

#include "tensorflow/compiler/mlir/tensorflow/utils/mlprogram_util.h"
#include "tensorflow/compiler/mlir/tools/kernel_gen/ir/tf_framework_ops.h"
#include "xla/mlir/framework/ir/xla_framework.h"
#include "xla/mlir_hlo/mhlo/IR/register.h"

namespace mlir {

void RegisterCommonToolingDialects(mlir::DialectRegistry& registry) {
  mlir::RegisterAllTensorFlowDialects(registry);

            "@local_xla//xla/service:hlo_profile_printer_data_cc",
        ] or []) + (include_standard_runtime_deps and [
            # TODO(cwhipkey): only depend on kernel code that the model actually
            # needed.
            "@local_xla//xla/service/cpu:runtime_conv2d",
            "@local_xla//xla/service/cpu:runtime_custom_call_status",
            "@local_xla//xla/service/cpu:runtime_key_value_sort",