// RUN: tf-opt %s -split-input-file -verify-diagnostics -tf-xla-validate-inputs

// expected-error @+1 {{expects no nested calls of entry functions as they prevent graph traversal in some passes from working correctly}}
func.func @nested_entry_functions() attributes {tf.entry_function = {}} {
  tf_executor.graph {

// RUN: tf-quant-opt %s -split-input-file -quant-convert-fake-quant-to-qdq -quant-lift-quantizable-spots-as-functions='target-opset=XLA' -quant-insert-quantized-functions -quant-quantize-composite-functions='target-opset=XLA' -symbol-dce -inline -tf-shape-inference -canonicalize -quant-replace-cast-hacks-with-tf-xla-ops -cse -quant-optimize | FileCheck %s

module attributes {tf.versions = {bad_consumers = [], min_consumer = 12 : i32, producer = 1219 : i32}, tf_saved_model.semantics} {

        "@llvm-project//llvm:Support",
        "@llvm-project//mlir:FuncDialect",
        "@llvm-project//mlir:IR",
        "@llvm-project//mlir:Support",
        "@local_xla//xla/service:hlo_parser",
        "@local_xla//xla/service:hlo_proto_cc",
        "@local_xla//xla/translate/hlo_to_mhlo:hlo_to_mlir_hlo",
    ],
)

# Smaller version of flatbuffer_translate which only converts flatbuffer to MLIR.
cc_library(

                "portion of the bridge. This is a logical no-op.";

  let description = [{
    Gathers metrics about the input MLIR to Phase 2 of the TFXLA Bridge, which
    does a strict semantic lowering from Tensorflow ops to XLA HLO.
  }];

  let constructor = "tensorflow::tf2xla::internal::CreateInputLoweringMetricsPass()";

      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"))};

  Option<int64_t> min_num_elements_for_weights_{

CreateTPUClusterFormationPass(bool strict_clusters = false);

// Creates a pass that extracts outside compilation (Host ops inside device
// cluster) at head/tail of Device cluster to run before/after XLA computation.
std::unique_ptr<mlir::OperationPass<mlir::ModuleOp>>
CreateExtractHeadTailOutsideCompilationPass();

// Creates a pass that extract outside compilation (Host ops inside cevice

#include "absl/memory/memory.h"
#include "tensorflow/compiler/jit/xla_activity.pb.h"
#include "tensorflow/compiler/jit/xla_activity_listener.h"

namespace tensorflow {
namespace {

// Listens to XLA activity and logs them using tensorflow::Logger.
class XlaActivityLoggingListener final : public XlaActivityListener {
 public:
  Status Listen(
      const XlaAutoClusteringActivity& auto_clustering_activity) override {

#include "mlir/Support/TypeID.h"  // from @llvm-project
#include "mlir/Transforms/GreedyPatternRewriteDriver.h"  // from @llvm-project
#include "tensorflow/compiler/mlir/quantization/stablehlo/passes/passes.h"
#include "xla/mlir_hlo/mhlo/transforms/rewriters.h"

//===----------------------------------------------------------------------===//
// The unfuse-mhlo-batch-norm Pass.

load("@local_xla//xla/tsl:tsl.default.bzl", "tsl_pybind_extension")
load("//tensorflow:pytype.default.bzl", "pytype_strict_library")
load("//tensorflow:strict.default.bzl", "py_strict_test")

package(
    # copybara:uncomment default_applicable_licenses = ["//tensorflow:license"],
    default_visibility = [
        ":friends",
        "//tensorflow/tools/pip_package:__subpackages__",
    ],
    licenses = ["notice"],
)

package_group(

      // cluster because we would not handle variable updates correctly.  Any
      // locks we have already acquired will be released when the VariableInfo
      // objects are destroyed.
      // TODO(b/128495870) Add support for passing aliased resource variables.
      return errors::Unimplemented("Duplicate variable passed to XLA cluster");
    }
    if (variables[i]->read_only()) {