#
# Licensed to the Apache Software Foundation (ASF) under one or more
# contributor license agreements.  See the NOTICE file distributed with
# this work for additional information regarding copyright ownership.
# The ASF licenses this file to You under the Apache License, Version 2.0
# (the "License"); you may not use this file except in compliance with
# the License.  You may obtain a copy of the License at
#
#    http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#

# cython: profile=True

"""Worker operations executor.

For internal use only; no backwards-compatibility guarantees.
"""

import sys
import traceback

import six

from apache_beam.internal import util
from apache_beam.pvalue import TaggedOutput
from apache_beam.transforms import DoFn
from apache_beam.transforms import core
from apache_beam.transforms.core import RestrictionProvider
from apache_beam.transforms.window import GlobalWindow
from apache_beam.transforms.window import TimestampedValue
from apache_beam.transforms.window import WindowFn
from apache_beam.utils.windowed_value import WindowedValue


class NameContext(object):
  """Holds the name information for a step."""

  def __init__(self, step_name):
    """Creates a new step NameContext.

    Args:
      step_name: The name of the step.
    """
    self.step_name = step_name

  def __eq__(self, other):
    return self.step_name == other.step_name

  def __ne__(self, other):
    return not self == other

  def __repr__(self):
    return 'NameContext(%s)' % self.__dict__

  def __hash__(self):
    return hash(self.step_name)

  def metrics_name(self):
    """Returns the step name used for metrics reporting."""
    return self.step_name

  def logging_name(self):
    """Returns the step name used for logging."""
    return self.step_name


# TODO(BEAM-4028): Move DataflowNameContext to Dataflow internal code.
class DataflowNameContext(NameContext):
  """Holds the name information for a step in Dataflow.

  This includes a step_name (e.g. s2), a user_name (e.g. Foo/Bar/ParDo(Fab)),
  and a system_name (e.g. s2-shuffle-read34)."""

  def __init__(self, step_name, user_name, system_name):
    """Creates a new step NameContext.

    Args:
      step_name: The internal name of the step (e.g. s2).
      user_name: The full user-given name of the step (e.g. Foo/Bar/ParDo(Far)).
      system_name: The step name in the optimized graph (e.g. s2-1).
    """
    super(DataflowNameContext, self).__init__(step_name)
    self.user_name = user_name
    self.system_name = system_name

  def __eq__(self, other):
    return (self.step_name == other.step_name and
            self.user_name == other.user_name and
            self.system_name == other.system_name)

  def __ne__(self, other):
    return not self == other

  def __hash__(self):
    return hash((self.step_name, self.user_name, self.system_name))

  def __repr__(self):
    return 'DataflowNameContext(%s)' % self.__dict__

  def logging_name(self):
    """Stackdriver logging relies on user-given step names (e.g. Foo/Bar)."""
    return self.user_name


class LoggingContext(object):
  """For internal use only; no backwards-compatibility guarantees."""

  def enter(self):
    pass

  def exit(self):
    pass


class Receiver(object):
  """For internal use only; no backwards-compatibility guarantees.

  An object that consumes a WindowedValue.

  This class can be efficiently used to pass values between the
  sdk and worker harnesses.
  """

  def receive(self, windowed_value):
    raise NotImplementedError


class MethodWrapper(object):
  """For internal use only; no backwards-compatibility guarantees.

  Represents a method that can be invoked by `DoFnInvoker`."""

  def __init__(self, obj_to_invoke, method_name):
    """
    Initiates a ``MethodWrapper``.

    Args:
      obj_to_invoke: the object that contains the method. Has to either be a
                    `DoFn` object or a `RestrictionProvider` object.
      method_name: name of the method as a string.
    """

    if not isinstance(obj_to_invoke, (DoFn, RestrictionProvider)):
      raise ValueError('\'obj_to_invoke\' has to be either a \'DoFn\' or '
                       'a \'RestrictionProvider\'. Received %r instead.',
                       obj_to_invoke)

    args, _, _, defaults = core.get_function_arguments(
        obj_to_invoke, method_name)
    defaults = defaults if defaults else []
    method_value = getattr(obj_to_invoke, method_name)
    self.method_value = method_value
    self.args = args
    self.defaults = defaults


class DoFnSignature(object):
  """Represents the signature of a given ``DoFn`` object.

  Signature of a ``DoFn`` provides a view of the properties of a given ``DoFn``.
  Among other things, this will give an extensible way for for (1) accessing the
  structure of the ``DoFn`` including methods and method parameters
  (2) identifying features that a given ``DoFn`` support, for example, whether
  a given ``DoFn`` is a Splittable ``DoFn`` (
  https://s.apache.org/splittable-do-fn) (3) validating a ``DoFn`` based on the
  feature set offered by it.
  """

  def __init__(self, do_fn):
    # We add a property here for all methods defined by Beam DoFn features.

    assert isinstance(do_fn, core.DoFn)
    self.do_fn = do_fn

    self.process_method = MethodWrapper(do_fn, 'process')
    self.start_bundle_method = MethodWrapper(do_fn, 'start_bundle')
    self.finish_bundle_method = MethodWrapper(do_fn, 'finish_bundle')

    restriction_provider = self._get_restriction_provider(do_fn)
    self.initial_restriction_method = (
        MethodWrapper(restriction_provider, 'initial_restriction')
        if restriction_provider else None)
    self.restriction_coder_method = (
        MethodWrapper(restriction_provider, 'restriction_coder')
        if restriction_provider else None)
    self.create_tracker_method = (
        MethodWrapper(restriction_provider, 'create_tracker')
        if restriction_provider else None)
    self.split_method = (
        MethodWrapper(restriction_provider, 'split')
        if restriction_provider else None)

    self._validate()

  def _get_restriction_provider(self, do_fn):
    result = _find_param_with_default(self.process_method,
                                      default_as_type=RestrictionProvider)
    return result[1] if result else None

  def _validate(self):
    self._validate_process()
    self._validate_bundle_method(self.start_bundle_method)
    self._validate_bundle_method(self.finish_bundle_method)

  def _validate_process(self):
    """Validate that none of the DoFnParameters are repeated in the function
    """
    for param in core.DoFn.DoFnParams:
      assert self.process_method.defaults.count(param) <= 1

  def _validate_bundle_method(self, method_wrapper):
    """Validate that none of the DoFnParameters are used in the function
    """
    for param in core.DoFn.DoFnParams:
      assert param not in method_wrapper.defaults

  def is_splittable_dofn(self):
    return any([isinstance(default, RestrictionProvider) for default in
                self.process_method.defaults])


class DoFnInvoker(object):
  """An abstraction that can be used to execute DoFn methods.

  A DoFnInvoker describes a particular way for invoking methods of a DoFn
  represented by a given DoFnSignature."""

  def __init__(self, output_processor, signature):
    self.output_processor = output_processor
    self.signature = signature

  @staticmethod
  def create_invoker(
      signature,
      output_processor=None,
      context=None, side_inputs=None, input_args=None, input_kwargs=None,
      process_invocation=True):
    """ Creates a new DoFnInvoker based on given arguments.

    Args:
        output_processor: an OutputProcessor for receiving elements produced by
                          invoking functions of the DoFn.
        signature: a DoFnSignature for the DoFn being invoked.
        context: Context to be used when invoking the DoFn (deprecated).
        side_inputs: side inputs to be used when invoking th process method.
        input_args: arguments to be used when invoking the process method. Some
                    of the arguments given here might be placeholders (for
                    example for side inputs) that get filled before invoking the
                    process method.
        input_kwargs: keyword arguments to be used when invoking the process
                      method. Some of the keyword arguments given here might be
                      placeholders (for example for side inputs) that get filled
                      before invoking the process method.
        process_invocation: If True, this function may return an invoker that
                            performs extra optimizations for invoking process()
                            method efficiently.
    """
    side_inputs = side_inputs or []
    default_arg_values = signature.process_method.defaults
    use_simple_invoker = not process_invocation or (
        not side_inputs and not input_args and not input_kwargs and
        not default_arg_values)
    if use_simple_invoker:
      return SimpleInvoker(output_processor, signature)
    else:
      return PerWindowInvoker(
          output_processor,
          signature, context, side_inputs, input_args, input_kwargs)

  def invoke_process(self, windowed_value, restriction_tracker=None,
                     output_processor=None,
                     additional_args=None, additional_kwargs=None):
    """Invokes the DoFn.process() function.

    Args:
      windowed_value: a WindowedValue object that gives the element for which
                      process() method should be invoked along with the window
                      the element belongs to.
      output_procesor: if provided given OutputProcessor will be used.
      additional_args: additional arguments to be passed to the current
                      `DoFn.process()` invocation, usually as side inputs.
      additional_kwargs: additional keyword arguments to be passed to the
                         current `DoFn.process()` invocation.
    """
    raise NotImplementedError

  def invoke_start_bundle(self):
    """Invokes the DoFn.start_bundle() method.
    """
    self.output_processor.start_bundle_outputs(
        self.signature.start_bundle_method.method_value())

  def invoke_finish_bundle(self):
    """Invokes the DoFn.finish_bundle() method.
    """
    self.output_processor.finish_bundle_outputs(
        self.signature.finish_bundle_method.method_value())

  def invoke_split(self, element, restriction):
    return self.signature.split_method.method_value(element, restriction)

  def invoke_initial_restriction(self, element):
    return self.signature.initial_restriction_method.method_value(element)

  def invoke_restriction_coder(self):
    return self.signature.restriction_coder_method.method_value()

  def invoke_create_tracker(self, restriction):
    return self.signature.create_tracker_method.method_value(restriction)


def _find_param_with_default(
    method, default_as_value=None, default_as_type=None):
  if ((default_as_value and default_as_type) or
      not (default_as_value or default_as_type)):
    raise ValueError(
        'Exactly one of \'default_as_value\' and \'default_as_type\' should be '
        'provided. Received %r and %r.', default_as_value, default_as_type)

  defaults = method.defaults
  default_as_value = default_as_value
  default_as_type = default_as_type
  ret = None
  for i, value in enumerate(defaults):
    if default_as_value and value == default_as_value:
      ret = (method.args[len(method.args) - len(defaults) + i], value)
    elif default_as_type and isinstance(value, default_as_type):
      index = len(method.args) - len(defaults) + i
      ret = (method.args[index], value)

  return ret


class SimpleInvoker(DoFnInvoker):
  """An invoker that processes elements ignoring windowing information."""

  def __init__(self, output_processor, signature):
    super(SimpleInvoker, self).__init__(output_processor, signature)
    self.process_method = signature.process_method.method_value

  def invoke_process(self, windowed_value, restriction_tracker=None,
                     output_processor=None,
                     additional_args=None, additional_kwargs=None):
    if not output_processor:
      output_processor = self.output_processor
    output_processor.process_outputs(
        windowed_value, self.process_method(windowed_value.value))


class PerWindowInvoker(DoFnInvoker):
  """An invoker that processes elements considering windowing information."""

  def __init__(self, output_processor, signature, context,
               side_inputs, input_args, input_kwargs):
    super(PerWindowInvoker, self).__init__(output_processor, signature)
    self.side_inputs = side_inputs
    self.context = context
    self.process_method = signature.process_method.method_value
    default_arg_values = signature.process_method.defaults
    self.has_windowed_inputs = (
        not all(si.is_globally_windowed() for si in side_inputs) or
        (core.DoFn.WindowParam in default_arg_values))

    # Try to prepare all the arguments that can just be filled in
    # without any additional work. in the process function.
    # Also cache all the placeholders needed in the process function.

    # Flag to cache additional arguments on the first element if all
    # inputs are within the global window.
    self.cache_globally_windowed_args = not self.has_windowed_inputs

    input_args = input_args if input_args else []
    input_kwargs = input_kwargs if input_kwargs else {}

    arguments = signature.process_method.args
    defaults = signature.process_method.defaults

    # Create placeholder for element parameter of DoFn.process() method.
    self_in_args = int(signature.do_fn.is_process_bounded())

    class ArgPlaceholder(object):
      def __init__(self, placeholder):
        self.placeholder = placeholder

    if core.DoFn.ElementParam not in default_arg_values:
      args_to_pick = len(arguments) - len(default_arg_values) - 1 - self_in_args
      args_with_placeholders = (
          [ArgPlaceholder(core.DoFn.ElementParam)] + input_args[:args_to_pick])
    else:
      args_to_pick = len(arguments) - len(defaults) - self_in_args
      args_with_placeholders = input_args[:args_to_pick]

    # Fill the OtherPlaceholders for context, window or timestamp
    remaining_args_iter = iter(input_args[args_to_pick:])
    for a, d in zip(arguments[-len(defaults):], defaults):
      if d == core.DoFn.ElementParam:
        args_with_placeholders.append(ArgPlaceholder(d))
      elif d == core.DoFn.WindowParam:
        args_with_placeholders.append(ArgPlaceholder(d))
      elif d == core.DoFn.TimestampParam:
        args_with_placeholders.append(ArgPlaceholder(d))
      elif d == core.DoFn.SideInputParam:
        # If no more args are present then the value must be passed via kwarg
        try:
          args_with_placeholders.append(next(remaining_args_iter))
        except StopIteration:
          if a not in input_kwargs:
            raise ValueError("Value for sideinput %s not provided" % a)
      else:
        # If no more args are present then the value must be passed via kwarg
        try:
          args_with_placeholders.append(next(remaining_args_iter))
        except StopIteration:
          pass
    args_with_placeholders.extend(list(remaining_args_iter))

    # Stash the list of placeholder positions for performance
    self.placeholders = [(i, x.placeholder) for (i, x) in enumerate(
        args_with_placeholders)
                         if isinstance(x, ArgPlaceholder)]

    self.args_for_process = args_with_placeholders
    self.kwargs_for_process = input_kwargs

  def invoke_process(self, windowed_value, restriction_tracker=None,
                     output_processor=None,
                     additional_args=None, additional_kwargs=None):
    if not additional_args:
      additional_args = []
    if not additional_kwargs:
      additional_kwargs = {}

    if not output_processor:
      output_processor = self.output_processor
    self.context.set_element(windowed_value)
    # Call for the process function for each window if has windowed side inputs
    # or if the process accesses the window parameter. We can just call it once
    # otherwise as none of the arguments are changing

    if restriction_tracker:
      restriction_tracker_param = _find_param_with_default(
          self.signature.process_method,
          default_as_type=core.RestrictionProvider)[0]
      if not restriction_tracker_param:
        raise ValueError(
            'A RestrictionTracker %r was provided but DoFn does not have a '
            'RestrictionTrackerParam defined', restriction_tracker)
      additional_kwargs[restriction_tracker_param] = restriction_tracker
    if self.has_windowed_inputs and len(windowed_value.windows) != 1:
      for w in windowed_value.windows:
        self._invoke_per_window(
            WindowedValue(windowed_value.value, windowed_value.timestamp, (w,)),
            additional_args, additional_kwargs, output_processor)
    else:
      self._invoke_per_window(
          windowed_value, additional_args, additional_kwargs, output_processor)

  def _invoke_per_window(
      self, windowed_value, additional_args,
      additional_kwargs, output_processor):
    if self.has_windowed_inputs:
      window, = windowed_value.windows
      side_inputs = [si[window] for si in self.side_inputs]
      side_inputs.extend(additional_args)
      args_for_process, kwargs_for_process = util.insert_values_in_args(
          self.args_for_process, self.kwargs_for_process,
          side_inputs)
    elif self.cache_globally_windowed_args:
      # Attempt to cache additional args if all inputs are globally
      # windowed inputs when processing the first element.
      self.cache_globally_windowed_args = False

      # Fill in sideInputs if they are globally windowed
      global_window = GlobalWindow()
      self.args_for_process, self.kwargs_for_process = (
          util.insert_values_in_args(
              self.args_for_process, self.kwargs_for_process,
              [si[global_window] for si in self.side_inputs]))
      args_for_process, kwargs_for_process = (
          self.args_for_process, self.kwargs_for_process)
    else:
      args_for_process, kwargs_for_process = (
          self.args_for_process, self.kwargs_for_process)
    # TODO(sourabhbajaj): Investigate why we can't use `is` instead of ==
    for i, p in self.placeholders:
      if p == core.DoFn.ElementParam:
        args_for_process[i] = windowed_value.value
      elif p == core.DoFn.WindowParam:
        args_for_process[i] = window
      elif p == core.DoFn.TimestampParam:
        args_for_process[i] = windowed_value.timestamp

    if additional_kwargs:
      if kwargs_for_process is None:
        kwargs_for_process = additional_kwargs
      else:
        for key in additional_kwargs:
          kwargs_for_process[key] = additional_kwargs[key]

    if kwargs_for_process:
      output_processor.process_outputs(
          windowed_value,
          self.process_method(*args_for_process, **kwargs_for_process))
    else:
      output_processor.process_outputs(
          windowed_value, self.process_method(*args_for_process))


class DoFnRunner(Receiver):
  """For internal use only; no backwards-compatibility guarantees.

  A helper class for executing ParDo operations.
  """

  def __init__(self,
               fn,
               args,
               kwargs,
               side_inputs,
               windowing,
               tagged_receivers=None,
               step_name=None,
               logging_context=None,
               state=None,
               scoped_metrics_container=None):
    """Initializes a DoFnRunner.

    Args:
      fn: user DoFn to invoke
      args: positional side input arguments (static and placeholder), if any
      kwargs: keyword side input arguments (static and placeholder), if any
      side_inputs: list of sideinput.SideInputMaps for deferred side inputs
      windowing: windowing properties of the output PCollection(s)
      tagged_receivers: a dict of tag name to Receiver objects
      step_name: the name of this step
      logging_context: a LoggingContext object
      state: handle for accessing DoFn state
      scoped_metrics_container: Context switcher for metrics container
    """
    # Need to support multiple iterations.
    side_inputs = list(side_inputs)

    from apache_beam.metrics.execution import ScopedMetricsContainer

    self.scoped_metrics_container = (
        scoped_metrics_container or ScopedMetricsContainer())
    self.step_name = step_name
    self.logging_context = logging_context or LoggingContext()
    self.context = DoFnContext(step_name, state=state)

    do_fn_signature = DoFnSignature(fn)

    # Optimize for the common case.
    main_receivers = tagged_receivers[None]
    output_processor = _OutputProcessor(
        windowing.windowfn, main_receivers, tagged_receivers)

    self.do_fn_invoker = DoFnInvoker.create_invoker(
        do_fn_signature, output_processor, self.context, side_inputs, args,
        kwargs)

  def receive(self, windowed_value):
    self.process(windowed_value)

  def process(self, windowed_value):
    try:
      self.logging_context.enter()
      self.scoped_metrics_container.enter()
      self.do_fn_invoker.invoke_process(windowed_value)
    except BaseException as exn:
      self._reraise_augmented(exn)
    finally:
      self.scoped_metrics_container.exit()
      self.logging_context.exit()

  def _invoke_bundle_method(self, bundle_method):
    try:
      self.logging_context.enter()
      self.scoped_metrics_container.enter()
      self.context.set_element(None)
      bundle_method()
    except BaseException as exn:
      self._reraise_augmented(exn)
    finally:
      self.scoped_metrics_container.exit()
      self.logging_context.exit()

  def start(self):
    self._invoke_bundle_method(self.do_fn_invoker.invoke_start_bundle)

  def finish(self):
    self._invoke_bundle_method(self.do_fn_invoker.invoke_finish_bundle)

  def _reraise_augmented(self, exn):
    if getattr(exn, '_tagged_with_step', False) or not self.step_name:
      raise
    step_annotation = " [while running '%s']" % self.step_name
    # To emulate exception chaining (not available in Python 2).
    original_traceback = sys.exc_info()[2]
    try:
      # Attempt to construct the same kind of exception
      # with an augmented message.
      new_exn = type(exn)(exn.args[0] + step_annotation, *exn.args[1:])
      new_exn._tagged_with_step = True  # Could raise attribute error.
    except:  # pylint: disable=bare-except
      # If anything goes wrong, construct a RuntimeError whose message
      # records the original exception's type and message.
      new_exn = RuntimeError(
          traceback.format_exception_only(type(exn), exn)[-1].strip()
          + step_annotation)
      new_exn._tagged_with_step = True
    six.reraise(type(new_exn), new_exn, original_traceback)


class OutputProcessor(object):

  def process_outputs(self, windowed_input_element, results):
    raise NotImplementedError


class _OutputProcessor(OutputProcessor):
  """Processes output produced by DoFn method invocations."""

  def __init__(self, window_fn, main_receivers, tagged_receivers):
    """Initializes ``_OutputProcessor``.

    Args:
      window_fn: a windowing function (WindowFn).
      main_receivers: a dict of tag name to Receiver objects.
      tagged_receivers: main receiver object.
    """
    self.window_fn = window_fn
    self.main_receivers = main_receivers
    self.tagged_receivers = tagged_receivers

  def process_outputs(self, windowed_input_element, results):
    """Dispatch the result of process computation to the appropriate receivers.

    A value wrapped in a TaggedOutput object will be unwrapped and
    then dispatched to the appropriate indexed output.
    """
    if results is None:
      return

    for result in results:
      tag = None
      if isinstance(result, TaggedOutput):
        tag = result.tag
        if not isinstance(tag, six.string_types):
          raise TypeError('In %s, tag %s is not a string' % (self, tag))
        result = result.value
      if isinstance(result, WindowedValue):
        windowed_value = result
        if (windowed_input_element is not None
            and len(windowed_input_element.windows) != 1):
          windowed_value.windows *= len(windowed_input_element.windows)
      elif isinstance(result, TimestampedValue):
        assign_context = WindowFn.AssignContext(result.timestamp, result.value)
        windowed_value = WindowedValue(
            result.value, result.timestamp,
            self.window_fn.assign(assign_context))
        if len(windowed_input_element.windows) != 1:
          windowed_value.windows *= len(windowed_input_element.windows)
      else:
        windowed_value = windowed_input_element.with_value(result)
      if tag is None:
        self.main_receivers.receive(windowed_value)
      else:
        self.tagged_receivers[tag].receive(windowed_value)

  def start_bundle_outputs(self, results):
    """Validate that start_bundle does not output any elements"""
    if results is None:
      return
    raise RuntimeError(
        'Start Bundle should not output any elements but got %s' % results)

  def finish_bundle_outputs(self, results):
    """Dispatch the result of finish_bundle to the appropriate receivers.

    A value wrapped in a TaggedOutput object will be unwrapped and
    then dispatched to the appropriate indexed output.
    """
    if results is None:
      return

    for result in results:
      tag = None
      if isinstance(result, TaggedOutput):
        tag = result.tag
        if not isinstance(tag, six.string_types):
          raise TypeError('In %s, tag %s is not a string' % (self, tag))
        result = result.value

      if isinstance(result, WindowedValue):
        windowed_value = result
      else:
        raise RuntimeError('Finish Bundle should only output WindowedValue ' +\
                           'type but got %s' % type(result))

      if tag is None:
        self.main_receivers.receive(windowed_value)
      else:
        self.tagged_receivers[tag].receive(windowed_value)


class _NoContext(WindowFn.AssignContext):
  """An uninspectable WindowFn.AssignContext."""
  NO_VALUE = object()

  def __init__(self, value, timestamp=NO_VALUE):
    self.value = value
    self._timestamp = timestamp

  @property
  def timestamp(self):
    if self._timestamp is self.NO_VALUE:
      raise ValueError('No timestamp in this context.')
    else:
      return self._timestamp

  @property
  def existing_windows(self):
    raise ValueError('No existing_windows in this context.')


class DoFnState(object):
  """For internal use only; no backwards-compatibility guarantees.

  Keeps track of state that DoFns want, currently, user counters.
  """

  def __init__(self, counter_factory):
    self.step_name = ''
    self._counter_factory = counter_factory

  def counter_for(self, aggregator):
    """Looks up the counter for this aggregator, creating one if necessary."""
    return self._counter_factory.get_aggregator_counter(
        self.step_name, aggregator)


# TODO(robertwb): Replace core.DoFnContext with this.
class DoFnContext(object):
  """For internal use only; no backwards-compatibility guarantees."""

  def __init__(self, label, element=None, state=None):
    self.label = label
    self.state = state
    if element is not None:
      self.set_element(element)

  def set_element(self, windowed_value):
    self.windowed_value = windowed_value

  @property
  def element(self):
    if self.windowed_value is None:
      raise AttributeError('element not accessible in this context')
    else:
      return self.windowed_value.value

  @property
  def timestamp(self):
    if self.windowed_value is None:
      raise AttributeError('timestamp not accessible in this context')
    else:
      return self.windowed_value.timestamp

  @property
  def windows(self):
    if self.windowed_value is None:
      raise AttributeError('windows not accessible in this context')
    else:
      return self.windowed_value.windows
