"@llvm-project//mlir:MlirOptLib",
        "@llvm-project//mlir:Support",
        "@llvm-project//mlir:Transforms",
        "@local_xla//xla/mlir/framework/ir:xla_framework",
        "@local_xla//xla/mlir/framework/transforms:passes",
        "@local_xla//xla/mlir_hlo:all_passes",
    ],
)

cc_library(
    name = "passes",
    visibility = [
        ":__subpackages__",

  if (xla_device == nullptr) {
    return errors::Internal(
        "Cannot get XLA metadata from non-XLA device \"", device->name(),
        "\". GetMetadata must only be called on an XLA device. Either an "
        "internal bug has been triggered, or an XLA-specific op has been "
        "placed on the wrong device.");
  }
  *metadata = &(xla_device->xla_metadata_);

#include "tensorflow/core/tpu/tpu_defs.h"

namespace tensorflow {
namespace {
using XlaDeviceCompiler =
    DeviceCompiler<xla::LocalExecutable, xla::LocalClient>;
using PjRtDeviceCompiler =
    DeviceCompiler<xla::PjRtLoadedExecutable, xla::PjRtClient>;

class XlaPlatformInfoTest : public ::testing::Test {
 protected:
  void SetUp() override {

// Provides definitions needed for use of the TensorFlow XLA
// device.

#ifndef TENSORFLOW_COMPILER_JIT_DEFS_H_
#define TENSORFLOW_COMPILER_JIT_DEFS_H_

namespace tensorflow {

// Name of attribute used to tag operators for compilation with XLA

// Implies must-compile semantics: either it will be compiled
// with XLA, or an error will be thrown.

limitations under the License.
==============================================================================*/

// Registers the XLA_CPU device, which is an XlaDevice instantiation that runs
// operators using XLA via the XLA "Host" (CPU) backend.

#include <array>

#include "absl/memory/memory.h"
#include "tensorflow/compiler/jit/defs.h"
#include "tensorflow/compiler/jit/flags.h"
#include "tensorflow/compiler/jit/kernels/xla_ops.h"

#include "tensorflow/compiler/tf2xla/shape_util.h"
#include "tensorflow/compiler/tf2xla/xla_op_registry.h"
#include "xla/stream_executor/device_memory.h"
#include "xla/stream_executor/platform_manager.h"
#include "xla/stream_executor/stream.h"
#include "xla/stream_executor/stream_executor.h"
#include "tensorflow/core/framework/tensor_testutil.h"
#include "tsl/lib/core/status_test_util.h"

namespace tensorflow {

#include "tensorflow/compiler/jit/xla_compilation_cache.pb.h"
#include "tensorflow/compiler/jit/xla_device_compiler_client.h"
#include "tensorflow/compiler/tf2xla/xla_compiler.h"
#include "xla/pjrt/pjrt_client.h"
#include "xla/service/hlo.pb.h"
#include "xla/util.h"
#include "tensorflow/core/framework/device.h"
#include "tensorflow/core/lib/strings/proto_serialization.h"
#include "tensorflow/core/platform/path.h"

#include "tensorflow/compiler/tf2xla/xla_helpers.h"
#include "xla/util.h"
#include "tensorflow/core/common_runtime/device.h"
#include "tensorflow/core/common_runtime/dma_helper.h"
#include "tensorflow/core/framework/tensor_reference.h"
#include "tsl/platform/statusor.h"

namespace tensorflow {

// The allocator used for Tensors assigned to the XLA device.
XlaDeviceAllocator::XlaDeviceAllocator(

// RUN: tf-mlir-translate -mlir-tf-to-hlo-text %s -tf-input-shapes=: -tf-xla-emit-return-tuple | FileCheck %s
// RUN: tf-mlir-translate -mlir-tf-to-hlo-text %s -tf-input-shapes=: -tf-xla-emit-use-tuple-args -tf-xla-emit-return-tuple | FileCheck -check-prefix=TUPLE-ARGS %s
// RUN: tf-mlir-translate -mlir-tf-to-hlo-text %s -tf-input-shapes=: | FileCheck -check-prefix=NO_RET_TUPLE %s

#include "tensorflow/compiler/mlir/tf2xla/internal/test_matchers.h"
#include "tensorflow/compiler/tf2xla/xla_compiler.h"
#include "tensorflow/compiler/tf2xla/xla_helpers.h"
#include "xla/client/client_library.h"
#include "xla/shape.h"
#include "xla/stream_executor/platform.h"
#include "xla/stream_executor/platform_manager.h"
#include "tensorflow/core/framework/tensor_shape.h"
#include "tensorflow/core/framework/types.pb.h"