// Whether soft placement is allowed. If true, the marked node is eligible for
// outside compilation.
inline constexpr llvm::StringRef kAllowSoftPlacementAttr =
    "allow_soft_placement";

// Marks a node for XLA compilation. The attribute value indicates the
// compilation device type.
inline constexpr llvm::StringRef kCompileDeviceTypeAttr =
    "_xla_compile_device_type";

#include "tensorflow/compiler/tf2xla/xla_expression.h"
#include "tensorflow/compiler/tf2xla/xla_helpers.h"
#include "tensorflow/compiler/tf2xla/xla_op_registry.h"
#include "xla/client/xla_builder.h"
#include "xla/mlir_hlo/mhlo/IR/hlo_ops.h"
#include "tensorflow/core/common_runtime/device.h"
#include "tensorflow/core/common_runtime/device_factory.h"
#include "tensorflow/core/common_runtime/device_mgr.h"

#include "mlir/Transforms/DialectConversion.h"  // from @llvm-project
#include "tensorflow/compiler/mlir/quantization/stablehlo/utils/tf_type_utils.h"
#include "tensorflow/compiler/mlir/tensorflow/ir/tf_ops.h"
#include "xla/mlir_hlo/mhlo/IR/hlo_ops.h"

namespace mlir::quant::stablehlo {
namespace {

using quant::tensorflow::IsTFQintType;
using quant::tensorflow::IsTFUniformQuantizedOp;

#define GEN_PASS_DEF_VERIFYQUANTLEGALIZATION

    'tensorflow/compiler/mlir/quantization/tensorflow',
    'tensorflow/compiler/mlir/tensorflow',
    'tensorflow/compiler/mlir/tfrt',
    'tensorflow/compiler/mlir/tools/kernel_gen',
    os.path.join(external_srcdir, 'local_xla/xla/mlir_hlo'),
    'tensorflow/core/ir/importexport/',
    'tensorflow/core/ir/tests/',
    'tensorflow/core/transforms/',
    'tensorflow/dtensor/mlir/tests',
]
config.mlir_tf_tools_dirs = [

#include "mlir/Support/LLVM.h"  // from @llvm-project
#include "mlir/Transforms/DialectConversion.h"  // from @llvm-project
#include "tensorflow/compiler/mlir/lite/transforms/passes.h"  // IWYU pragma: keep
#include "xla/mlir_hlo/mhlo/IR/hlo_ops.h"  // IWYU pragma: keep

namespace mlir {
namespace odml {

// Ensure an attribute named attr_name exists and it is of type AttrType.

// converting them to NHWC formats along with extra optimizations such as
// constant folding the transpose->convolution pattern. This is useful when
// downstream pipeline (e.g. XLA) is more optimized when accepting NHWC formats.
void AddProcessNchwTensorPasses(OpPassManager& pm);

// Registers quantization pass pipelines. This is only required when running

#include "tensorflow/compiler/mlir/quantization/tensorflow/cc/run_passes.h"
#include "tensorflow/compiler/mlir/quantization/tensorflow/python/py_function_lib.h"
#include "xla/mlir_hlo/mhlo/transforms/passes.h"
#include "tsl/platform/errors.h"
#include "tsl/platform/statusor.h"

namespace mlir::quant::stablehlo {

using ::stablehlo::quantization::GetReportFilePath;

#include "tensorflow/compiler/mlir/tensorflow/transforms/passes.h"
#include "tensorflow/compiler/mlir/tensorflow/transforms/tf_saved_model_passes.h"
#include "tensorflow/compiler/mlir/tf2xla/transforms/passes.h"
#include "xla/mlir_hlo/mhlo/transforms/passes.h"

namespace mlir {
namespace odml {

void AddTFToStablehloPasses(OpPassManager& pm, bool skip_resize,
                            bool smuggle_disallowed_ops) {

#include "tensorflow/compiler/mlir/tensorflow/ir/tf_device.h"
#include "tensorflow/compiler/mlir/tensorflow/ir/tf_ops.h"
#include "tensorflow/compiler/mlir/tensorflow/utils/attribute_utils.h"
#include "xla/mlir_hlo/mhlo/IR/hlo_ops.h"

#define DEBUG_TYPE "tf-tpu-annotate-dynamic-shape-inputs"

namespace mlir {
namespace TFTPU {

namespace {

#define GEN_PASS_DEF_TPUANNOTATEDYNAMICSHAPEINPUTSPASS

#include "tensorflow/compiler/mlir/lite/transforms/passes.h"  // IWYU pragma: keep
#include "tensorflow/compiler/mlir/tensorflow/ir/tf_ops.h"  // IWYU pragma: keep
#include "xla/mlir_hlo/mhlo/IR/hlo_ops.h"

namespace mlir {
namespace odml {
namespace {

// This file is generated from `passes.td` and provides the implementation base
// class.
#define GEN_PASS_DEF_LEGALIZEHLOTOTFLITEPASS