"@com_google_absl//absl/status",
        "@com_google_googletest//:gtest_main",
        "@local_tsl//tsl/platform:statusor",
        "@local_xla//xla/client:xla_computation",
        "@local_xla//xla/service:hlo_proto_cc",
    ],
)

cc_library(
    name = "mlir_pass_instrumentation",
    srcs = ["mlir_pass_instrumentation.cc"],
    hdrs = ["mlir_pass_instrumentation.h"],
    deps = [

#include "tensorflow/compiler/mlir/tf2xla/internal/utils/test_metadata_config.h"
#include "tensorflow/compiler/tf2xla/xla_compiler.h"
#include "tensorflow/compiler/tf2xla/xla_helpers.h"
#include "xla/client/client_library.h"
#include "xla/stream_executor/platform_manager.h"
#include "tensorflow/core/lib/monitoring/cell_reader.h"
#include "tensorflow/core/lib/monitoring/test_utils.h"
#include "tensorflow/core/platform/env.h"

namespace tensorflow {

// Compiles/lowers a given Tensorflow graph/function/cluster into a compiled XLA
// compilation (HLO) using the XlaCompiler and compiles the resulting
// XlaCompilationResult into an `ExecutableType` (eg. xla::LocalExecutable) by
// calling `ClientType` (eg. xla::LocalClient).
//
// Caches the compiled XlaCompilationResult and Executable using a

    return "quant-replace-cast-hacks-with-tf-xla-ops";
  }
  StringRef getDescription() const final {
    // This is a brief description of the pass.
    return "Replace mixed-type Conv and Matmul cast hacks with TF XLA ops.";
  }

  void runOnOperation() override;
};

// Generates params for the XLA Convolution op.
void PrepareXlaConvParams(OpBuilder &builder, Location loc, ArrayAttr strides,

// Checks if a device string is a TPU replicated core device.
bool IsTPUReplicatedCore(llvm::StringRef device);

// Checks if `type` is allowed for XLA. String and resources are not XLA types.
// There are other TF types that are not XLA types which will be removed by
// successive passes in TF/XLA bridge phase 2.
bool TypeValidForXLA(const mlir::Type& type);

// Returns the map from core to the host that is associated with the

}

def XlaInlineDeviceOpsPass : Pass<"tf-xla-inline-device-ops", "ModuleOp"> {
  let summary = "Inline all Cluster op based in the parent region";
  let constructor = "TFDevice::CreateXlaInlineDeviceOpsPass()";
  let dependentDialects = ["tf_device::TensorFlowDeviceDialect"];
}

def XlaRewritePass : Pass<"tf-xla-rewrite", "mlir::ModuleOp"> {
  let summary = "Rewrites partition calls into Xla launch ops to make the attached function run on XLA.";

#include "tensorflow/compiler/mlir/tf2xla/transforms/passes.h"
#include "tensorflow/compiler/mlir/tf2xla/transforms/xla_legalize_targets.h"
#include "xla/mlir_hlo/mhlo/IR/hlo_ops.h"
#include "xla/mlir_hlo/mhlo/transforms/rewriters.h"
#include "xla/mlir_hlo/mhlo/utils/type_conversion.h"
#include "tensorflow/core/lib/monitoring/counter.h"

namespace mlir {
namespace mhlo {
namespace {

#include "tensorflow/compiler/mlir/tensorflow/ir/tf_ops.h"
#include "tensorflow/compiler/mlir/tensorflow/ir/tf_traits.h"
#include "tensorflow/compiler/mlir/tensorflow/utils/xla_sharding_util.h"
#include "xla/client/sharding_builder.h"
#include "xla/xla_data.pb.h"
#include "tsl/platform/errors.h"

namespace tensorflow {
namespace tf2xla {
namespace internal {
namespace {

#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"

      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() {