import okhttp3.internal.toNonNegativeInt

/**
 * Given a request and cached response, this figures out whether to use the network, the cache, or
 * both.
 *
 * Selecting a cache strategy may add conditions to the request (like the "If-Modified-Since" header
 * for conditional GETs) or warnings to the cached response (if the cached data is potentially
 * stale).
 */
class CacheStrategy internal constructor(

                  }
                });

    // To start, fill up the cache.
    // Each miss both increments the counter and causes the map to grow by one,
    // so until evictions begin, the size of the map is the greatest return
    // value seen so far
    while (cache.getUnchecked(nextRandomKey()) < maximumSize) {}

    requests.set(0);
    misses.set(0);
  }

  @Benchmark
  int time(int reps) {

# ==============================================================================
# Sourcing this enables Bazel remote cache (read and write)
# Note that "_push" cache configs write to GCS buckets and require
# authentication. If you are not a Googler, source "public_cache" to enable the
# public read-only cache.
# The cache configs are different for MacOS and Linux
if [[ $(uname -s) == "Darwin" ]]; then

def test_sub_counter_no_cache():
    counter_holder["counter"] = 0
    response = client.get("/sub-counter-no-cache/")
    assert response.status_code == 200, response.text
    assert response.json() == {"counter": 2, "subcounter": 1}
    response = client.get("/sub-counter-no-cache/")
    assert response.status_code == 200, response.text
    assert response.json() == {"counter": 4, "subcounter": 3}

package cmd

import (
	"context"
)

const (
	memTotal     = "total"
	memUsed      = "used"
	memFree      = "free"
	memBuffers   = "buffers"
	memCache     = "cache"
	memUsedPerc  = "used_perc"
	memShared    = "shared"
	memAvailable = "available"
)

var (
	memTotalMD     = NewGaugeMD(memTotal, "Total memory on the node")

    }

    /**
     * Benchmark that tests cache benefits by repeatedly setting properties on the same class.
     * This better demonstrates the caching improvements.
     */
    @Benchmark
    public void benchmarkOriginalHelperRepeatedOperations() throws Exception {
        // Test cache benefits by using same class multiple times
        for (int i = 0; i < 10; i++) {

  }

  @Test
  fun cache() {
    val cache = Cache(File("/cache/"), Integer.MAX_VALUE.toLong())
    cache.initialize()
    cache.delete()
    cache.evictAll()
    val urls: MutableIterator<String> = cache.urls()
    val writeAbortCount: Int = cache.writeAbortCount()
    val writeSuccessCount: Int = cache.writeSuccessCount()
    val size: Long = cache.size()
    val maxSize: Long = cache.maxSize()

 * </p>
 *
 * <pre>
 * BeanDesc beanDesc = BeanDescFactory.getBeanDesc(Foo.class);
 * </pre>
 * <p>
 * {@link BeanDesc} is cached. To clear the cache, call {@link DisposableUtil#dispose()}.
 * </p>
 *
 * @author higa
 * @see BeanDesc
 * @see DisposableUtil
 */
public abstract class BeanDescFactory {

    /**
     * Do not instantiate.

#      Ex. no_upload      -- Disable all uploads, usually for temporary CI issues

# Recommended: use a local+remote cache.
#
#   Bazel will cache your builds in tensorflow/build_output/cache,
#   and will also try using public build cache results to speed up
#   your builds. This usually saves a lot of time, especially when
#   re-running tests. However, note that:
#

    /**
     * Get the cache key for this referral
     *
     * @return cache key
     */
    String getKey();

    /**
     * Set the cache key for this referral
     *
     * @param key
     *            cache key
     */
    void setKey(String key);

    /**
     * Set the cache map for this referral
     *
     * @param map the cache map to associate with this referral
     */