text: import collections
  import contextlib
  import threading
  import types
  from typing import (
      Any, Callable, Dict, Generator, List, Mapping, Optional, Sequence,
      Tuple, Type, Union,
  )
  from typing import overload
  import warnings
  
  import numpy
  import torch
  
  
  Scalar = Union[torch.Tensor, numpy.ndarray, numpy.floating, float]
  FloatLikeValue = Union[Scalar, float]
  Value = Union[Scalar, Callable[[], float]]
  Observation = Dict[str, Value]
  
  
  _thread_local = threading.local()
  
  
  @overload
  def _nograd(value: FloatLikeValue) -> FloatLikeValue:
      ...
  
  
  @overload
  def _nograd(value: Value) -> Value:
      ...
  
  
  def _nograd(
          value: Union[FloatLikeValue, Value]) -> Union[FloatLikeValue, Value]:
      if isinstance(value, torch.Tensor):
          return value.detach()
      return value
  
  
  class Reporter:
  
      """Object to which observed values are reported.
  
      Reporter is used to collect values that users want to watch. The reporter
      object holds a mapping from value names to the actually observed values.
      We call this mapping `observations`.
  
      When a value is passed to the reporter, an object called `observer` can be
      optionally attached. In this case, the name of the observer is added as the
      prefix of the value name. The observer name should be registered
      beforehand.
  
      See the following example:
  
      >>> from pytorch_pfn_extras.reporting import Reporter, report, report_scope
      >>>
      >>> reporter = Reporter()
      >>> observer = object()  # it can be an arbitrary (reference) object
      >>> reporter.add_observer('my_observer', observer)
      >>> observation = {}
      >>> with reporter.scope(observation):
      ...     reporter.report({'x': 1}, observer)
      ...
      >>> observation
      {'my_observer/x': 1}
  
      There are also a global API to add values:
  
      >>> reporter = Reporter()
      >>> observation = {}
      >>> with reporter:
      ...     with report_scope(observation):
      ...         report({'x': 1})
      ...
      >>> observation
      {'x': 1}
  
      The most important application of Reporter is to report observed values
      from each link or chain in the training and validation procedures.
      and some extensions prepare their own
      Reporter object with the hierarchy of the target module registered as
      observers. We can use :func:`report` function inside any nn.Module
      to report the observed values (e.g., training loss, accuracy, activation
      statistics, etc.).
  
      Attributes:
          observation: Dictionary of observed values.
  
      """
  
      def __init__(self) -> None:
          self._observer_names: Dict[int, str] = {}
          self.observation: Observation = {}
  
      def __enter__(self) -> None:
          """Makes this reporter object current."""
          _get_reporters().append(self)
  
      def __exit__(
              self,
              exc_type: Optional[Type[BaseException]],
              exc_value: Optional[BaseException],
              traceback: Optional[types.TracebackType],
      ) -> None:
          """Recovers the previous reporter object to the current."""
          _get_reporters().pop()
  
      @contextlib.contextmanager
      def scope(self, observation: Observation) -> Generator[None, None, None]:
          """Creates a scope to report observed values to ``observation``.
  
          This is a context manager to be passed to ``with`` statements. In this
          scope, the observation dictionary is changed to the given one.
  
          It also makes this reporter object current.
  
          Args:
              observation (dict): Observation dictionary. All observations
                  reported inside of the ``with`` statement are written to this
                  dictionary.
  
          """
          old = self.observation
          self.observation = observation
          self.__enter__()
          try:
              yield
          finally:
              self.__exit__(None, None, None)
              self.observation = old
  
      def add_observer(self, name: str, observer: torch.nn.Module) -> None:
          """Registers an observer of values.
  
          Observer defines a scope of names for observed values. Values observed
          with the observer are registered with names prefixed by the observer
          name.
  
          Args:
              name (str): Name of the observer.
              observer: The observer object. Note that the reporter distinguishes
                  the observers by their object ids (i.e., ``id(owner)``), rather
                  than the object equality.
  
          """
          self._observer_names[id(observer)] = name
  
      def add_observers(
              self,
              prefix: str,
              observers: Sequence[Tuple[str, torch.nn.Module]]
      ) -> None:
          """Registers multiple observers at once.
  
          This is a convenient method to register multiple objects at once.
  
          Args:
              prefix (str): Prefix of each name of observers.
              observers: Iterator of name and observer pairs.
  
          """
          for name, observer in observers:
              self._observer_names[id(observer)] = prefix + name
  
      def report(
              self,
              values: Mapping[str, Value],
              observer: Optional[torch.nn.Module] = None,
      ) -> None:
          """Reports observed values.
  
          The values are written with the key, prefixed by the name of the
          observer object if given.
  
          .. note::
             If a value is of type :class:`~torch.Tensor`, the
             variable is copied without preserving the computational graph and
             the new variable object purged from the graph is stored to the
             observer.
  
          Args:
              values (dict): Dictionary of observed values.
              observer: Observer object. Its object ID is used to retrieve the
                  observer name, which is used as the prefix of the registration
                  name of the observed value.
  
          """
          values = {k: _nograd(v) for k, v in values.items()}
  
          if observer is not None:
              observer_id = id(observer)
              if observer_id not in self._observer_names:
                  raise KeyError(
                      'Given observer is not registered to the reporter.')
              observer_name = self._observer_names[observer_id]
              for key, value in values.items():
                  name = '%s/%s' % (observer_name, key)
                  self.observation[name] = value
          else:
              self.observation.update(values)
  
  
  def _get_reporters() -> List[Reporter]:
      try:
          reporters: List[Reporter] = _thread_local.reporters
      except AttributeError:
          reporters = _thread_local.reporters = []
      return reporters
  
  
  def get_current_reporter() -> Reporter:
      """Returns the current reporter object."""
      return _get_reporters()[-1]
  
  
  def report(
          values: Mapping[str, Value],
          observer: Optional[torch.nn.Module] = None,
  ) -> None:
      """Reports observed values with the current reporter object.
  
      Any reporter object can be set current by the ``with`` statement. This
      function calls the :meth:`Reporter.report` method of the current reporter.
      If no reporter object is current, this function does nothing.
  
      .. admonition:: Example
  
         The most typical example is a use within `nn.Module`. Suppose that
         a module is registered to the current reporter as an observer (for
         example, the target module of the optimizer is automatically
         registered to the main reporter. We can report
         some values from the link as follows::
  
            class MyRegressor:
                def __init__(self, predictor):
                    super().__init__(predictor=predictor)
  
                def __call__(self, x, y):
                    # This chain just computes the mean absolute and squared
                    # errors between the prediction and y.
                    pred = self.predictor(x)
                    abs_error = F.sum(abs(pred - y)) / len(x)
                    loss = F.mean_squared_error(pred, y)
  
                    # Report the mean absolute and squared errors.
                    reporter.report({
                        'abs_error': abs_error,
                        'squared_error': loss,
                    }, self)
  
                    return loss
  
         If the module is named ``'main'`` in the hierarchy these reported values
         are named ``'main/abs_error'`` and ``'main/squared_error'``.
  
      Args:
          values (dict): Dictionary of observed values.
          observer: Observer object. Its object ID is used to retrieve the
              observer name, which is used as the prefix of the registration name
              of the observed value.
  
      """
      reporters = _get_reporters()
      if reporters:
          current = reporters[-1]
          current.report(values, observer)
  
  
  @contextlib.contextmanager
  def report_scope(observation: Observation) -> Generator[None, None, None]:
      """Returns a report scope with the current reporter.
  
      This is equivalent to ``get_current_reporter().scope(observation)``,
      except that it does not make the reporter current redundantly.
  
      """
      current = _get_reporters()[-1]
      old = current.observation
      current.observation = observation
      yield
      current.observation = old
  
  
  class Summary:
  
      """Online summarization of a sequence of scalars.
  
      Summary computes the statistics of given scalars online.
  
      """
  
      def __init__(self) -> None:
          self._x: Scalar = 0.0
          self._x2: Scalar = 0.0
          self._n: Scalar = 0
          self._deferred: List[Tuple[Callable[[], float], Scalar]] = []
  
      def _add_deferred_values(self) -> None:
          for fn, weight in self._deferred:
              value = fn()
              self.add(value, weight)
          self._deferred.clear()
  
      def add(self, value: Value, weight: Scalar = 1) -> None:
          """Adds a scalar value.
  
          Args:
              value: Scalar value to accumulate. It is either a NumPy scalar or
                  a zero-dimensional array (on CPU or GPU).
              weight: An optional weight for the value. It is a NumPy scalar or
                  a zero-dimensional array (on CPU or GPU).
                  Default is 1 (integer).
  
          """
          if callable(value):
              self._deferred.append((value, weight))
              return
  
          self._x += weight * value
          self._x2 += weight * value * value
          self._n += weight
  
      def compute_mean(self) -> Scalar:
          """Computes the mean."""
          self._add_deferred_values()
  
          x, n = self._x, self._n
          return x / n
  
      def make_statistics(self) -> Tuple[Scalar, Scalar]:
          """Computes and returns the mean and standard deviation values.
  
          Returns:
              tuple: Mean and standard deviation values.
  
          """
          self._add_deferred_values()
  
          x, n = self._x, self._n
          mean = x / n
          var = self._x2 / n - mean * mean
          if isinstance(var, torch.Tensor):
              return mean, torch.sqrt(var)
          else:
              return mean, numpy.sqrt(var)
  
      def state_dict(self) -> Dict[str, Any]:
          self._add_deferred_values()
          state = {}
          try:
              # Save the stats as python scalars in order to avoid
              # different device errors when loading them back
              state = {'_x': float(self._x),
                       '_x2': float(self._x2),
                       '_n': int(self._n)}
          except KeyError:
              warnings.warn('The previous statistics are not saved.')
          return state
  
      def load_state_dict(self, to_load: Dict[str, Any]) -> None:
          # Casting here is because of backward compatibility
          # Restore previously taken snapshots with autoload
          self._add_deferred_values()
          self._x = float(_nograd(to_load['_x']))
          self._x2 = float(_nograd(to_load['_x2']))
          self._n = int(_nograd(to_load['_n']))
  
  
  class DictSummary:
  
      """Online summarization of a sequence of dictionaries.
  
      ``DictSummary`` computes the statistics of a given set of scalars online.
      It only computes the statistics for scalar values and variables of scalar
      values in the dictionaries.
  
      """
  
      def __init__(self) -> None:
          self._summaries: Dict[str, Summary] = collections.defaultdict(Summary)
  
      def add(self, d: Mapping[str, Union[Value, Tuple[Value, Scalar]]]) -> None:
          """Adds a dictionary of scalars.
  
          Args:
              d (dict): Dictionary of scalars to accumulate. Only elements of
                 scalars, zero-dimensional arrays, and variables of
                 zero-dimensional arrays are accumulated. When the value
                 is a tuple, the second element is interpreted as a weight.
  
          """
          summaries = self._summaries
          for k, v in d.items():
              w: Scalar = 1
              if isinstance(v, tuple):
                  v, w = v
                  if not numpy.isscalar(w) and not getattr(w, 'ndim', -1) == 0:
                      raise ValueError(
                          'Given weight to {} was not scalar.'.format(k))
              if numpy.isscalar(v) or getattr(v, 'ndim', -1) == 0:
                  summaries[k].add(v, weight=w)
              elif callable(v):
                  summaries[k].add(v, weight=w)
  
      def compute_mean(self) -> Dict[str, Scalar]:
          """Creates a dictionary of mean values.
  
          It returns a single dictionary that holds a mean value for each entry
          added to the summary.
  
          Returns:
              dict: Dictionary of mean values.
  
          """
          return {name: summary.compute_mean()
                  for name, summary in self._summaries.items()}
  
      def make_statistics(self) -> Dict[str, Scalar]:
          """Creates a dictionary of statistics.
  
          It returns a single dictionary that holds mean and standard deviation
          values for every entry added to the summary. For an entry of name
          ``'key'``, these values are added to the dictionary by names ``'key'``
          and ``'key.std'``, respectively.
  
          Returns:
              dict: Dictionary of statistics of all entries.
  
          """
          stats = {}
          for name, summary in self._summaries.items():
              mean, std = summary.make_statistics()
              stats[name] = mean
              stats[name + '.std'] = std
  
          return stats
  
      def state_dict(self) -> Dict[str, Any]:
          return {
              name: summ.state_dict() for name, summ in self._summaries.items()}
  
      def load_state_dict(self, to_load: Dict[str, Any]) -> None:
          self._summaries.clear()
          for name, summ_state in to_load.items():
              self._summaries[name].load_state_dict(summ_state)
  