* close or flush the reader.
   *
   * <p>This is identical to {@link #copy(Readable, Appendable)} but optimized for these specific
   * types. CharBuffer has poor performance when being written into or read out of so round tripping
   * all the bytes through the buffer takes a long time. With these specialized types we can just
   * use a char array.
   *

 * between hash algorithms and the data they act on, so alternate hash algorithms can't be easily
 * substituted. Also, implementations of {@code hashCode} tend to be poor-quality, in part because
 * they end up depending on <i>other</i> existing poor-quality {@code hashCode} implementations,
 * including those in many JDK classes.
 *
 * <p>{@code Object.hashCode} implementations tend to be very fast, but have weak collision

        TlsVersion.TLS_1_2,
        reversed,
      )

    makeRequest(client)

    val expectedConnectionCipherSuites = expectedConnectionCipherSuites(client)
    // Will choose a poor cipher suite but not plaintext.
//    assertThat(handshake.cipherSuite).isEqualTo("TLS_ECDHE_ECDSA_WITH_AES_128_GCM_SHA256")
    assertThat(handshakeEnabledCipherSuites).containsExactly(

   * WeakReference} (by default, strong references are used).
   *
   * <p>Weak values will be garbage collected once they are weakly reachable. This makes them a poor
   * candidate for caching.
   *
   * <p><b>Warning:</b> when this method is used, the resulting map will use identity ({@code ==})

   *       servers {@code alpha}, {@code bravo}, and {@code charlie} and you occasionally need to
   *       take each of the servers offline, {@code consistentHash} will be a poor fit: It provides
   *       no way for you to specify which of the three buckets is disappearing. Thus, if your
   *       buckets change from {@code [alpha, bravo, charlie]} to {@code [bravo, charlie]}, it will

     * additional values interleaved with those shown above.)
     */
    @CheckForNull
    abstract N visitNext(Deque<Iterator<? extends N>> horizon);
  }

  /** Poor man's method reference for {@code Deque::addFirst} and {@code Deque::addLast}. */
  private enum InsertionOrder {
    FRONT {
      @Override
      <T> void insertInto(Deque<T> deque, T value) {

    // The Dolphin algorithm is attractive because it does the fewest array reads and writes: each
    // array slot is read and written exactly once. However, it can have very poor memory locality:
    // benchmarking shows it can take 7 times longer than the other two in some cases. The other two
    // do n swaps, minus a delta (0 or 2 for Reversal, gcd(d, n) for Successive), so that's about

    // and reduces the false-positive probability.
    static final int MAX_RUN_MULTIPLIER = 13;

    /**
     * Checks the whole hash table for poor hash distribution. Takes O(n) in the worst case, O(n /
     * log n) on average.
     *
     * <p>The online hash flooding detecting in RegularSetBuilderImpl.add can detect e.g. many

   * WeakReference} (by default, strong references are used).
   *
   * <p>Weak values will be garbage collected once they are weakly reachable. This makes them a poor
   * candidate for caching; consider {@link #softValues} instead.
   *
   * <p><b>Note:</b> when this method is used, the resulting cache will use identity ({@code ==})
   * comparison to determine equality of values.

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