//
// You should have received a copy of the GNU Affero General Public License
// along with this program.  If not, see <http://www.gnu.org/licenses/>.

// Package amztime implements AWS specific time parsing and deviations
package amztime

import (
	"errors"
	"net/http"
	"time"
)

// Supported amz date formats.
var amzDateFormats = []string{
	// Do not change this order, x-amz-date format is usually in

 *   <li>{@linkplain State#RUNNING RUNNING} -&gt;
 *   <li>{@linkplain State#STOPPING STOPPING} -&gt;
 *   <li>{@linkplain State#TERMINATED TERMINATED}
 * </ul>
 *
 * <p>There are deviations from this if there are failures or if {@link Service#stopAsync} is called
 * before the {@link Service} reaches the {@linkplain State#RUNNING RUNNING} state. The set of legal

 *   <li>{@linkplain State#RUNNING RUNNING} -&gt;
 *   <li>{@linkplain State#STOPPING STOPPING} -&gt;
 *   <li>{@linkplain State#TERMINATED TERMINATED}
 * </ul>
 *
 * <p>There are deviations from this if there are failures or if {@link Service#stopAsync} is called
 * before the {@link Service} reaches the {@linkplain State#RUNNING RUNNING} state. The set of legal

      for (i in rangePos until rangeLimit step 4) {
        rangeStarts += compactTable.ranges[i].code
      }
      assertThat(rangeStarts).isEqualTo(rangeStarts.sorted())
    }
  }

  @Test fun deviations() {
    assertThat("ß".map()).isEqualTo("ß")
    assertThat("ς".map()).isEqualTo("ς")
    assertThat("\u200c".map()).isEqualTo("\u200c")
    assertThat("\u200d".map()).isEqualTo("\u200d")
  }

   * R*R)} of the population standard deviation of {@code y}, where {@code R} is the Pearson's
   * correlation coefficient (as given by {@link #pearsonsCorrelationCoefficient()}).
   *
   * <p>The corresponding root-mean-square error in {@code x} as a function of {@code y} is a
   * fraction {@code sqrt(1/(R*R) - 1)} of the population standard deviation of {@code x}. This fit

/**
 * A decoded [mapping table] that can perform the [mapping step] of IDNA processing.
 *
 * This implementation is optimized for readability over efficiency.
 *
 * This implements non-transitional processing by preserving deviation characters.
 *
 * This implementation's STD3 rules are configured to `UseSTD3ASCIIRules=false`. This is
 * permissive and permits the `_` character.
 *

   * R*R)} of the population standard deviation of {@code y}, where {@code R} is the Pearson's
   * correlation coefficient (as given by {@link #pearsonsCorrelationCoefficient()}).
   *
   * <p>The corresponding root-mean-square error in {@code x} as a function of {@code y} is a
   * fraction {@code sqrt(1/(R*R) - 1)} of the population standard deviation of {@code x}. This fit

  /**
   * Returns the <a
   * href="http://en.wikipedia.org/wiki/Standard_deviation#Corrected_sample_standard_deviation">
   * corrected sample standard deviation</a> of the values. If this dataset is a sample drawn from a
   * population, this is an estimator of the population standard deviation of the population which
   * is less biased than {@link #populationStandardDeviation()} (the unbiased estimator depends on

   * R*R)} of the population standard deviation of {@code y}, where {@code R} is the Pearson's
   * correlation coefficient (as given by {@link #pearsonsCorrelationCoefficient()}).
   *
   * <p>The corresponding root-mean-square error in {@code x} as a function of {@code y} is a
   * fraction {@code sqrt(1/(R*R) - 1)} of the population standard deviation of {@code x}. This fit

  /**
   * Returns the <a
   * href="http://en.wikipedia.org/wiki/Standard_deviation#Corrected_sample_standard_deviation">
   * corrected sample standard deviation</a> of the values. If this dataset is a sample drawn from a
   * population, this is an estimator of the population standard deviation of the population which
   * is less biased than {@link #populationStandardDeviation()} (the unbiased estimator depends on