"@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

// RUN: tf-quant-opt %s -quant-insert-quantized-functions='quantization-method=weight_only target-opset=XLA' | FileCheck %s

// Empty module
module {
  func.func @simple_fn(%arg0: tensor<*xf32>) -> tensor<*xf32> {
    func.return %arg0 : tensor<*xf32>
  }
}

// CHECK-NOT: func private @internal_dequantize_f32
// CHECK-NOT: func private @internal_conv3d_fn
// CHECK-NOT: func private @internal_batch_matmul_fn

#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(

# RUN: tf-mlir-translate -graphdef-to-mlir -tf-enable-shape-inference-on-import=false %s -tf-graph-as-function | tf-mlir-translate -mlir-tf-graph-to-hlo-text -tf-input-shapes='' -tf-input-data-types=DT_FLOAT -tf-xla-emit-return-tuple | FileCheck %s

node {
  name: "arg"
  op: "_Arg"
  attr {
    key: "T"
    value {
      type: DT_FLOAT
    }
  }
  attr {
    key: "index"
    value {
      i: 0
    }
  }
}
node {

    if (lowercased_title.includes('jax')) {
      assignees.push('hawkinsp', 'yashk2810', 'skye');
    }
    if (lowercased_title.includes('xla') || lowercased_title.includes('gpu')) {
      assignees.push('cheshire', 'gcforster', 'reedwm', 'chsigg', 'xla-rotation');
    }
    if (lowercased_title.includes('tf')) {
      assignees.push('rohan100jain', 'bfontain');
    }
  }