#include "tensorflow/c/experimental/stream_executor/stream_executor.h"
#include "tensorflow/c/tf_status_helper.h"
#include "xla/stream_executor/executor_cache.h"
#include "xla/stream_executor/platform.h"
#include "xla/stream_executor/stream_common.h"
#include "xla/stream_executor/stream_executor.h"
#include "tsl/platform/statusor.h"

namespace stream_executor {

#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/c/tf_status_helper.h"
#include "xla/stream_executor/executor_cache.h"
#include "xla/stream_executor/host_memory_allocation.h"
#include "xla/stream_executor/memory_allocation.h"
#include "xla/stream_executor/platform.h"
#include "xla/stream_executor/platform_manager.h"
#include "xla/stream_executor/stream.h"
#include "xla/stream_executor/stream_executor.h"
#include "xla/stream_executor/stream_executor_common.h"

#     --config=dbg --per_file_copt=+tensorflow/core/kernels/identity_op.*@-g
# Since this .bazelrc file is synced between the tensorflow/tensorflow repo and
# the openxla/xla repo, also include debug info for files under xla/.
build:dbg --per_file_copt=+.*,-tensorflow.*,-xla.*@-g0
build:dbg --per_file_copt=+tensorflow/core/kernels.*@-g0
# for now, disable arm_neon. see: https://github.com/tensorflow/tensorflow/issues/33360

// RUN: tf-opt %s -split-input-file -tf-xla-broadcast | FileCheck %s

bool ShapeInference::InferShapeForXlaGatherOp(XlaGatherOp op) {
  xla::Shape input_shape = xla::TypeToShape(op.getOperand().getType());
  if (input_shape == xla::Shape() || input_shape.is_unbounded_dynamic())
    return false;

  xla::Shape start_indices_shape =
      xla::TypeToShape(op.getStartIndices().getType());
  if (start_indices_shape == xla::Shape()) return false;

  xla::GatherDimensionNumbers gather_dim_numbers;

#include "tensorflow/compiler/mlir/tf2xla/transforms/utils.h"
#include "xla/client/lib/conv_grad_size_util.h"
#include "xla/client/padding.h"
#include "xla/client/sharding_builder.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/translate/hlo_to_mhlo/attribute_importer.h"
#include "xla/xla_data.pb.h"
#include "tensorflow/core/framework/kernel_shape_util.h"

        "@llvm-project//mlir:Support",
        "@llvm-project//mlir:TransformUtils",
        "@llvm-project//mlir:Transforms",
        "@local_xla//xla:status",
        "@local_xla//xla:statusor",
        "@local_xla//xla/mlir_hlo",
        "@stablehlo//:stablehlo_ops",
    ],
)

cc_library(
    name = "tensorflow_lite_optimize",
    srcs = [
        "transforms/generated_optimize.inc",
        "transforms/optimize.cc",

}

def TF_XlaRecvOp : TF_Op<"XlaRecv", [TF_RecvSideEffect]> {
  let summary = [{
Receives the named tensor from another XLA computation. Wraps the XLA Recv
  }];

  let description = [{
operator documented at
 https://www.tensorflow.org/performance/xla/operation_semantics#recv .
  }];

  let arguments = (ins
    StrAttr:$tensor_name,
    TF_ShapeAttr:$shape
  );