while (merged.hasNext()) {
          C next = merged.next();
          boolean duplicate = lastValue != null && comparator.compare(next, lastValue) == 0;

          // Keep looping till we find a non-duplicate value.
          if (!duplicate) {
            lastValue = next;
            return lastValue;
          }
        }

        lastValue = null; // clear reference to unused data

        while (merged.hasNext()) {
          C next = merged.next();
          boolean duplicate = lastValue != null && comparator.compare(next, lastValue) == 0;

          // Keep looping till we find a non-duplicate value.
          if (!duplicate) {
            lastValue = next;
            return lastValue;
          }
        }

        lastValue = null; // clear reference to unused data

      interpreted as a boolean indicator, assume that the user has made a
      scripting error, and will continue to provide invalid input.
      Raise the error to avoid infinitely looping.
  """
  if not question:
    question = 'Do you wish to build TensorFlow with {} support?'.format(
        query_item)
  if not yes_reply:

    *   Added APIs for switching between interactive logging and absl logging.
        By default, Keras always writes the logs to stdout. However, this is not
        optimal in a non-interactive environment, where you don't have access to
        stdout, but can only view the logs. You can use
        `tf.keras.utils.disable_interactive_logging()` to write the logs to ABSL
        logging. You can also use `tf.keras.utils.enable_interactive_logging()`

	logger.LogIf(ctx, "tier", err, errKind...)
}

func kmsLogIf(ctx context.Context, err error, errKind ...any) {
	logger.LogIf(ctx, "kms", err, errKind...)
}

// KMSLogger permits access to kms module specific logging
type KMSLogger struct{}

// LogOnceIf is the implementation of LogOnceIf, accessible using the Logger interface
func (l KMSLogger) LogOnceIf(ctx context.Context, err error, id string, errKind ...any) {

  "durations" : [ {
    "testProject" : "excludeRuleMergingBuild",
    "linux" : 232
  } ]
}, {
  "scenario" : "org.gradle.performance.regression.corefeature.LargeDependencyGraphPerformanceTest.resolve large dependency graph (parallel = false, locking = true)",
  "durations" : [ {
    "testProject" : "excludeRuleMergingBuild",

## 🔧 Configuration

### Logging Configuration
CoreLib supports multiple logging frameworks. Configure your preferred logger:

```java
// Use with SLF4J (add slf4j-api dependency)
Logger logger = Logger.getLogger(MyClass.class);

// Use with Commons Logging (add commons-logging dependency)  
Logger logger = Logger.getLogger(MyClass.class);

// Use with Java Util Logging (built-in)

import java.util.List;
import java.util.Map;

import org.apache.logging.log4j.LogManager;
import org.apache.logging.log4j.Logger;
import org.codelibs.fess.crawler.container.CrawlerContainer;

import jakarta.annotation.Resource;

/**
 * Creates and manages crawler clients for web crawling operations.
 * This class handles the registration and loading of crawler client factories and their associated clients.
 * <p>

/**
 * Security audit logger for SMB operations.
 * Provides structured logging of security-relevant events with sensitive data masking.
 *
 * Features:
 * - Structured JSON logging
 * - Sensitive data masking
 * - Event categorization
 * - Performance metrics
 * - Compliance-ready audit trail
 * - Asynchronous logging with bounded queue
 * - Object pooling to reduce GC pressure
 */
public class SecurityAuditLogger {

import java.nio.file.StandardCopyOption;
import java.util.Comparator;
import java.util.stream.Stream;
import java.util.zip.ZipEntry;
import java.util.zip.ZipInputStream;

import org.apache.logging.log4j.LogManager;
import org.apache.logging.log4j.Logger;
import org.codelibs.core.lang.StringUtil;
import org.codelibs.core.stream.StreamUtil;
import org.codelibs.fess.exception.ThemeException;
import org.codelibs.fess.helper.PluginHelper.Artifact;