// RUN: tf-mlir-translate -mlir-tf-to-hlo-text %s -tf-input-shapes=10,19:19,10 -tf-xla-emit-use-tuple-args -tf-xla-emit-return-tuple | FileCheck %s

module attributes {tf.versions = {producer = 179 : i32}} {
  func.func @main(%arg0: tensor<10x19xf32>, %arg1: tensor<19x10xf32> {mhlo.is_same_data_across_replicas = true}) -> tensor<10x19xf32> {
    %0 = "tf.Shape"(%arg0) : (tensor<10x19xf32>) -> tensor<2xi64>

enum OpSet {
  OP_SET_UNSPECIFIED = 0;  // go/do-include-enum-unspecified
  // Uses TF ops that mimic quantization behavior. Used when the corresponding
  // integer op is not yet present.
  TF = 1;
  // Uses TF XLA ops
  XLA = 2;
  // Uses TF Uniform Quantized ops
  UNIFORM_QUANTIZED = 3;
  // Uses the StableHLO Quantizer. StableHLO Quantizer will be available as
  // an option in the TF Quantizer in StableHLO Quantizer v1.

#include "tensorflow/compiler/mlir/tosa/tf_tfl_passes.h"
#include "tensorflow/compiler/mlir/tosa/tfl_passes.h"
#include "tensorflow/compiler/mlir/tosa/transforms/passes.h"
#include "xla/mlir/framework/transforms/passes.h"
#include "xla/mlir_hlo/mhlo/transforms/passes.h"

int main(int argc, char **argv) {
  tensorflow::InitMlir y(&argc, &argv);

  mlir::registerAllPasses();
  mlir::registerTransformsPasses();

#include "tensorflow/core/common_runtime/optimization_registry.h"

namespace tensorflow {

// PRE_PLACEMENT passes:

// EncapsulateXlaComputationsPass rewrites computations generated by the
// xla.compile() Python code into XlaLaunch nodes.
REGISTER_OPTIMIZATION(OptimizationPassRegistry::PRE_PLACEMENT, 36,
                      EncapsulateXlaComputationsPass);

// from

// 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