* limitations under the License.
 */

package org.gradle.internal.logging.source;

import org.gradle.api.logging.LogLevel;
import org.gradle.internal.logging.config.LoggingSourceSystem;
import org.slf4j.bridge.SLF4JBridgeHandler;

import java.util.HashMap;
import java.util.Map;
import java.util.logging.Level;
import java.util.logging.LogManager;
import java.util.logging.Logger;

/**

namespace {

using quant::tensorflow::IsTFQintType;
using quant::tensorflow::IsTFUniformQuantizedOp;

#define GEN_PASS_DEF_VERIFYQUANTLEGALIZATION
#include "tensorflow/compiler/mlir/quantization/stablehlo/passes/bridge/passes.h.inc"

class VerifyQuantLegalization
    : public impl::VerifyQuantLegalizationBase<VerifyQuantLegalization> {
 public:
  void runOnOperation() override;
};

bool IsQuantType(Type type) {

      return failure();

    // Populate set_is_same_data_across_replicas
    // Note: this information is duplicated and can be removed from the proto
    // and here once MLIR bridge phase 2 doesn't fallback to the old bridge.
    auto attr = op.getFuncOp().getArgAttrOfType<mlir::BoolAttr>(
        index, replication_attr_name);
    arg->set_is_same_data_across_replicas(attr != nullptr && attr.getValue());

// RUN: tf-opt %s -tf-replicated-clustering-bridge-v1 | FileCheck %s

module attributes {tf.devices = ["/job:localhost/replica:0/task:0/device:CPU:0", "/job:localhost/replica:0/task:0/device:TPU:0", "/job:localhost/replica:0/task:0/device:TPU:1", "/job:localhost/replica:0/task:0/device:TPU_SYSTEM:0"], tf.versions = {bad_consumers = [], min_consumer = 0 : i32, producer = 296 : i32}} {
  func.func @main() {
// CHECK: arith.constant
// CHECK: TPUCompile
// CHECK: TPUExecute

#include "mlir/Tools/mlir-opt/MlirOptMain.h"  // from @llvm-project
#include "tensorflow/compiler/mlir/tensorflow/dialect_registration.h"
#include "tensorflow/compiler/mlir/tensorflow/transforms/bridge.h"
#include "tensorflow/compiler/mlir/tensorflow/transforms/passes.h"
#include "tensorflow/core/framework/device.h"
#include "tensorflow/core/lib/core/status_test_util.h"
#include "tensorflow/core/platform/env.h"

#include "mlir/IR/MLIRContext.h"  // from @llvm-project
#include "mlir/Pass/PassManager.h"  // from @llvm-project
#include "mlir/Support/LogicalResult.h"  // from @llvm-project
#include "tensorflow/compiler/mlir/quantization/stablehlo/passes/bridge/passes.h"
#include "tensorflow/compiler/mlir/register_common_dialects.h"
#include "tensorflow/compiler/mlir/tensorflow/utils/serialize_mlir_module_utils.h"
#include "tensorflow/core/lib/core/status_test_util.h"

    bool use_tuple_args, bool use_resource_updates_for_aliases,
    const XlaShapeLayoutHelpers::ShapeDeterminationFns shape_determination_fns,
    XlaCompilationResult* compilation_result);

// Runs MLIR Bridge on a MLIR module.
//
// If lower_to_xla_hlo is true then compiles down into XLA HLO, generates all
// accompanying metadata and stores them in CompilationResult.
//

#include "tensorflow/compiler/mlir/lite/quantization/ir/QuantOps.h"
#include "tensorflow/compiler/mlir/quantization/stablehlo/cc/pass_pipeline.h"
#include "tensorflow/compiler/mlir/quantization/stablehlo/passes/bridge/passes.h"
#include "tensorflow/compiler/mlir/quantization/stablehlo/passes/passes.h"
#include "tensorflow/compiler/mlir/quantization/stablehlo/passes/testing/passes.h"
#include "tensorflow/compiler/mlir/tensorflow/ir/tf_dialect.h"

import java.nio.file.Files;

import org.apache.maven.MavenTestHelper;
import org.apache.maven.api.settings.InputSource;
import org.apache.maven.artifact.repository.layout.DefaultRepositoryLayout;
import org.apache.maven.bridge.MavenRepositorySystem;
import org.apache.maven.model.Profile;
import org.apache.maven.project.DefaultProjectBuilder;
import org.apache.maven.project.DefaultProjectBuildingRequest;

	// Hirschberg and Larmore's conference paper,
	// using the same names as in the paper.
	f := []score{{0, 0}}
	g := func(i, j int) score { return add(f[i], weight(i, j)) }

	bridge := func(a, b, c int) bool {
		k := c + sort.Search(len(words)+1-c, func(k int) bool {
			k += c
			return cmp(g(a, k), g(b, k)) > 0
		})
		if k > len(words) {
			return true
		}