text: """
  Average value meter
  """
  import numpy as np
  
  from catalyst.tools.meters import meter
  
  
  class AverageValueMeter(meter.Meter):
      """
      Average value meter stores mean and standard deviation
      for population of input values.
      Meter updates are applied online, one value for each update.
      Values are not cached, only the last added.
      """
  
      def __init__(self):
          """Constructor method for the ``AverageValueMeter`` class."""
          super(AverageValueMeter, self).__init__()
          self.n = 0
          self.val = 0.0
          self.mean = np.nan
          self.mean_old = 0.0
          self.m_s = 0.0
          self.std = np.nan
          self.n_samples = 0
  
      def add(self, value, batch_size) -> None:
          """Add a new observation.
  
          Updates of mean and std are going online, with
          `Welford's online algorithm
          <https://en.wikipedia.org/wiki/Algorithms_for_calculating_variance>`_.
  
          Args:
              value (float): value for update,
                  can be scalar number or PyTorch tensor
              batch_size (int): batch size for update
  
          .. note::
              Because of algorithm design,
              you can update meter values with only one value a time.
          """
          self.val = value
          self.n += 1
          self.n_samples += batch_size
  
          if self.n == 1:
              # Force a copy in torch/numpy
              self.mean = 0.0 + value  # noqa: WPS345
              self.std = 0.0
              self.mean_old = self.mean
              self.m_s = 0.0
          else:
              self.mean = self.mean_old + (
                  value - self.mean_old
              ) * batch_size / float(self.n_samples)
              self.m_s += (
                  (value - self.mean_old) * (value - self.mean) * batch_size
              )
              self.mean_old = self.mean
              self.std = np.sqrt(self.m_s / (self.n_samples - 1.0))
  
      def value(self):
          """Returns meter values.
  
          Returns:
              Tuple[float, float]: tuple of mean and std
              that have been updated online.
          """
          return self.mean, self.std
  
      def reset(self):
          """Resets the meter to default settings."""
          self.n = 0
          self.val = 0.0
          self.mean = np.nan
          self.mean_old = 0.0
          self.m_s = 0.0
          self.std = np.nan
          self.n_samples = 0
  
  
  __all__ = ["AverageValueMeter"]
  