// Only one of EmbeddingSequencing and EmbeddingPipelining will actually
  // run and the logic is in EmbeddingPipeliningPass. If the pipelining pass
  // runs, embedding attributes are stripped and the sequencing pass will have
  // no effect. If the pipelining pass doesn't run, embedding attributes are
  // preserved and the sequencing rewrite will trigger.
  pm.addPass(mlir::TFDevice::CreateEmbeddingPipeliningPass());

See the License for the specific language governing permissions and
limitations under the License.
==============================================================================*/
include "mlir/Pass/PassBase.td"

def VerifyClusteringPass : Pass<"verify-clustering-pass", "mlir::func::FuncOp"> {

  let summary = "Verify that the Bridge output is correct and errors if verification fails.";

  let description = [{

#include "llvm/ADT/ArrayRef.h"
#include "llvm/ADT/StringRef.h"
#include "mlir/Dialect/Func/IR/FuncOps.h"  // from @llvm-project
#include "mlir/IR/BuiltinOps.h"  // from @llvm-project
#include "mlir/Pass/Pass.h"  // from @llvm-project
#include "mlir/Pass/PassManager.h"  // from @llvm-project
#include "tensorflow/compiler/tf2xla/layout_util.h"
#include "tensorflow/compiler/tf2xla/xla_argument.h"
#include "tensorflow/compiler/tf2xla/xla_helpers.h"

#include "mlir/Dialect/Quant/QuantOps.h"  // from @llvm-project
#include "mlir/Dialect/Quant/QuantTypes.h"  // from @llvm-project
#include "mlir/IR/Operation.h"  // from @llvm-project
#include "mlir/Pass/Pass.h"  // from @llvm-project
#include "mlir/Support/LLVM.h"  // from @llvm-project
#include "mlir/Transforms/GreedyPatternRewriteDriver.h"  // from @llvm-project
#include "tensorflow/compiler/mlir/lite/quantization/ir/QuantOps.h"

#include "mlir/IR/PatternMatch.h"  // from @llvm-project
#include "mlir/IR/SymbolTable.h"  // from @llvm-project
#include "mlir/IR/Value.h"  // from @llvm-project
#include "mlir/Pass/Pass.h"  // from @llvm-project
#include "mlir/Pass/PassRegistry.h"  // from @llvm-project
#include "mlir/Rewrite/FrozenRewritePatternSet.h"  // from @llvm-project
#include "mlir/Support/LLVM.h"  // from @llvm-project

		fprintFunc(p, f)
		fi.Close()
	}
}

type pass struct {
	name     string
	fn       func(*Func)
	required bool
	disabled bool
	time     bool            // report time to run pass
	mem      bool            // report mem stats to run pass
	stats    int             // pass reports own "stats" (e.g., branches removed)

    }

    def "credentials can be specified via DSL"() {
        httpBuildCacheServer.withBasicAuth("user", "pass")
        settingsFile << """
            buildCache {
                remote.credentials {
                    username = "user"
                    password = "pass"
                }
            }
        """

        when:
        withBuildCache().run "jar"
        then:

			pass:  true,
		},
		// Test 4 - supported s3 type with post policy.
		{
			authT: authTypePostPolicy,
			pass:  true,
		},
		// Test 5 - supported s3 type with streaming signed.
		{
			authT: authTypeStreamingSigned,
			pass:  true,
		},
		// Test 6 - supported s3 type with signature v2.
		{
			authT: authTypeSignedV2,
			pass:  true,
		},

func CheckReadable(pass *analysis.Pass, filename string) error {
	if slicesContains(pass.OtherFiles, filename) ||
		slicesContains(pass.IgnoredFiles, filename) {
		return nil
	}
	for _, f := range pass.Files {
		// TODO(adonovan): use go1.20 f.FileStart
		if pass.Fset.File(f.Pos()).Name() == filename {
			return nil
		}
	}

  // TODO(hinsu): Investigate if we really need SCCP pass before shape inference
  // and can do with just one pass after the shape inference.
  pm.addPass(mlir::createSCCPPass());
  // Guarantee all functions have one use, which enables shape inference.
  pm.addPass(mlir::TF::CreateGuaranteeAllFuncsOneUsePass());
  // Run shape inference pass before tensorlist decomposition to get buffer