<component name="ProjectDictionaryState">
  <dictionary name="sergej.jaskiewicz">
    <words>
      <w>lowerings</w>
      <w>unmuted</w>
    </words>
  </dictionary>

  EXPECT_EQ(non_categorized_count, 428);
}

// Just a counter test to see which ops have duplicate lowerings. This isn't a
// correctness test versus a test to easily ensure the op registry is kept
// in sync.
TEST_F(LegalizationOpConfigTest, CountTypesWhichHaveBothMlirAndTf2xlaFallback) {
  int double_lowering_count = 0;

  DialectRegistry dialect_registry;
  dialect_registry.insert<mlir::TF::TensorFlowDialect>();

// Populates TensorFlow lowering patterns to lower some of the TensorFlow
// operations that can be represented by means of other TensorFlow operations.
// This pattern collection preserves those TensorFlow operations that will later
// be lowered to equivalent operations in CHLO or MHLO. This allows for
// HLO-specific lowerings.
void PopulateTFLoweringBeforeHLOPatterns(MLIRContext *context,

import org.jetbrains.kotlin.resolve.BindingContext

@OptIn(KaAnalysisNonPublicApi::class)
fun MutableSet<KtFile>.collectReachableInlineDelegatedPropertyAccessors() {
    if (isEmpty()) return

    // One of the compiler lowerings, namely `PropertyReferenceLowering`,
    // optimizes usages of property references in some cases,
    // and if a containing delegated property accessor is inline,
    // it might need this accessor's bytecode.
    //

  let constructor = "::mlir::tf_test::CreateInitTextFileToImportSavedModelTestPass()";
}

def TestTensorFlowLowerTFPass : Pass<"test-tf-lower-tf", "mlir::func::FuncOp"> {
  let summary = "Test pass for TF->TF lowerings";
  let constructor = "::mlir::tf_test::CreateTestTFLowerTFPass()";

  let options = [
    Option<"default_patterns_", "default-patterns", "bool", /*default=*/"true",
           "Activate the default TF->TF patterns">,

std::unique_ptr<OperationPass<ModuleOp>> createLegalizeTFPass(
    bool legalize_chlo = true,
    std::optional<StringRef> tf2xla_fallback_device_type = std::nullopt,
    bool prefer_tf2xla = false);

/// Adds the TF to TF lowerings and TF to XLA rewrite patterns to the pattern
/// list.
void PopulateLegalizeTfPatterns(MLIRContext* context,
                                RewritePatternSet* patterns);

def InputLoweringMetricsPass : Pass<"input-lowering-metrics-pass", "mlir::func::FuncOp"> {

  let summary = "Collects various metrics about the input to the lowering "
                "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.
  }];

}

// BatchMatMulV2 has native as well as fallback lowering patterns available.
// The fallback pattern uses dot_general without broadcast on operands and then
// transposes the output which is faster. However, the fallback pattern doesn't
// support dynamic shaped operands like the native lowering. Verify that
// fallback lowering is preferred for static shaped operands when available.

// CHECK-LABEL: batchmatmulv2

	}
}

// checkLower checks for unlowered opcodes and fails if we find one.
func checkLower(f *Func) {
	// Needs to be a separate phase because it must run after both
	// lowering and a subsequent dead code elimination (because lowering
	// rules may leave dead generic ops behind).
	for _, b := range f.Blocks {
		for _, v := range b.Values {
			if !opcodeTable[v.Op].generic {
				continue // lowered
			}
			switch v.Op {

// asmcheck

// Copyright 2020 The Go Authors. All rights reserved.
// Use of this source code is governed by a BSD-style
// license that can be found in the LICENSE file.

// Don't allow 0 masks in shift lowering rules on ppc64x.
// See issue 42610.

package codegen

func f32(a []int32, i uint32) {
        g := func(p int32) int32 {
                i = uint32(p) * (uint32(p) & (i & 1))
                return 1
        }