text: #
  # 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.
  """
  
  from __future__ import absolute_import
  
  import traceback
  from builtins import next
  from builtins import object
  from builtins import zip
  
  from future.utils import raise_with_traceback
  from past.builtins import unicode
  
  from apache_beam.internal import util
  from apache_beam.options.value_provider import RuntimeValueProvider
  from apache_beam.pvalue import TaggedOutput
  from apache_beam.transforms import DoFn
  from apache_beam.transforms import core
  from apache_beam.transforms import userstate
  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.counters import Counter
  from apache_beam.utils.counters import CounterName
  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):
      # TODO(BEAM-5949): Needed for Python 2 compatibility.
      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):
      # TODO(BEAM-5949): Needed for Python 2 compatibility.
      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 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)
  
      fullargspec = core.get_function_arguments(
          obj_to_invoke, method_name)
  
      # TODO(BEAM-5878) support kwonlyargs on Python 3.
      args = fullargspec[0]
      defaults = fullargspec[3]
  
      defaults = defaults if defaults else []
      method_value = getattr(obj_to_invoke, method_name)
      self.method_value = method_value
      self.args = args
      self.defaults = defaults
  
      self.has_userstate_arguments = False
      self.state_args_to_replace = {}
      self.timer_args_to_replace = {}
      for kw, v in zip(args[-len(defaults):], defaults):
        if isinstance(v, core.DoFn.StateParam):
          self.state_args_to_replace[kw] = v.state_spec
          self.has_userstate_arguments = True
        elif isinstance(v, core.DoFn.TimerParam):
          self.timer_args_to_replace[kw] = v.timer_spec
          self.has_userstate_arguments = True
  
    def invoke_timer_callback(self, user_state_context, key, window):
      # TODO(ccy): support WindowParam, TimestampParam and side inputs.
      if self.has_userstate_arguments:
        kwargs = {}
        for kw, state_spec in self.state_args_to_replace.items():
          kwargs[kw] = user_state_context.get_state(state_spec, key, window)
        for kw, timer_spec in self.timer_args_to_replace.items():
          kwargs[kw] = user_state_context.get_timer(timer_spec, key, window)
        return self.method_value(**kwargs)
      else:
        return self.method_value()
  
  
  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()
  
      # Handle stateful DoFns.
      self._is_stateful_dofn = userstate.is_stateful_dofn(do_fn)
      self.timer_methods = {}
      if self._is_stateful_dofn:
        # Populate timer firing methods, keyed by TimerSpec.
        _, all_timer_specs = userstate.get_dofn_specs(do_fn)
        for timer_spec in all_timer_specs:
          method = timer_spec._attached_callback
          self.timer_methods[timer_spec] = MethodWrapper(do_fn, method.__name__)
  
    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)
      self._validate_stateful_dofn()
  
    def _validate_process(self):
      """Validate that none of the DoFnParameters are repeated in the function
      """
      param_ids = [d.param_id for d in self.process_method.defaults
                   if isinstance(d, core._DoFnParam)]
      if len(param_ids) != len(set(param_ids)):
        raise ValueError(
            'DoFn %r has duplicate process method parameters: %s.' % (
                self.do_fn, param_ids))
  
    def _validate_bundle_method(self, method_wrapper):
      """Validate that none of the DoFnParameters are used in the function
      """
      for param in core.DoFn.DoFnProcessParams:
        if param in method_wrapper.defaults:
          raise ValueError(
              'DoFn.process() method-only parameter %s cannot be used in %s.' %
              (param, method_wrapper))
  
    def _validate_stateful_dofn(self):
      userstate.validate_stateful_dofn(self.do_fn)
  
    def is_splittable_dofn(self):
      return any([isinstance(default, RestrictionProvider) for default in
                  self.process_method.defaults])
  
    def is_stateful_dofn(self):
      return self._is_stateful_dofn
  
    def has_timers(self):
      _, all_timer_specs = userstate.get_dofn_specs(self.do_fn)
      return bool(all_timer_specs)
  
  
  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
      self.user_state_context = None
  
    @staticmethod
    def create_invoker(
        signature,
        output_processor=None,
        context=None, side_inputs=None, input_args=None, input_kwargs=None,
        process_invocation=True,
        user_state_context=None):
      """ 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.
          user_state_context: The UserStateContext instance for the current
                              Stateful DoFn.
      """
      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 and not signature.is_stateful_dofn())
      if use_simple_invoker:
        return SimpleInvoker(output_processor, signature)
      else:
        return PerWindowInvoker(
            output_processor,
            signature, context, side_inputs, input_args, input_kwargs,
            user_state_context)
  
    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_user_timer(self, timer_spec, key, window, timestamp):
      self.output_processor.process_outputs(
          WindowedValue(None, timestamp, (window,)),
          self.signature.timer_methods[timer_spec].invoke_timer_callback(
              self.user_state_context, key, window))
  
    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
    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, user_state_context):
      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) or
          signature.is_stateful_dofn())
      self.user_state_context = user_state_context
  
      # 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)
        elif isinstance(d, core.DoFn.StateParam):
          args_with_placeholders.append(ArgPlaceholder(d))
        elif isinstance(d, core.DoFn.TimerParam):
          args_with_placeholders.append(ArgPlaceholder(d))
        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)
  
      # Extract key in the case of a stateful DoFn. Note that in the case of a
      # stateful DoFn, we set during __init__ self.has_windowed_inputs to be
      # True. Therefore, windows will be exploded coming into this method, and
      # we can rely on the window variable being set above.
      if self.user_state_context:
        try:
          key, unused_value = windowed_value.value
        except (TypeError, ValueError):
          raise ValueError(
              ('Input value to a stateful DoFn must be a KV tuple; instead, '
               'got %s.') % (windowed_value.value,))
  
      # 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
        elif isinstance(p, core.DoFn.StateParam):
          args_for_process[i] = (
              self.user_state_context.get_state(p.state_spec, key, window))
        elif isinstance(p, core.DoFn.TimerParam):
          args_for_process[i] = (
              self.user_state_context.get_timer(p.timer_spec, key, window))
  
      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,
                 operation_name=None,
                 user_state_context=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: DEPRECATED [BEAM-4728]
        state: handle for accessing DoFn state
        scoped_metrics_container: DEPRECATED
        operation_name: The system name assigned by the runner for this operation.
        user_state_context: The UserStateContext instance for the current
                            Stateful DoFn.
      """
      # Need to support multiple iterations.
      side_inputs = list(side_inputs)
  
      self.step_name = step_name
      self.context = DoFnContext(step_name, state=state)
  
      do_fn_signature = DoFnSignature(fn)
  
      # Optimize for the common case.
      main_receivers = tagged_receivers[None]
  
      # TODO(BEAM-3937): Remove if block after output counter released.
      if 'outputs_per_element_counter' in RuntimeValueProvider.experiments:
        # TODO(BEAM-3955): Make step_name and operation_name less confused.
        output_counter_name = (CounterName('per-element-output-count',
                                           step_name=operation_name))
        per_element_output_counter = state._counter_factory.get_counter(
            output_counter_name, Counter.DATAFLOW_DISTRIBUTION).accumulator
      else:
        per_element_output_counter = None
  
      output_processor = _OutputProcessor(
          windowing.windowfn, main_receivers, tagged_receivers,
          per_element_output_counter)
  
      if do_fn_signature.is_stateful_dofn() and not user_state_context:
        raise Exception(
            'Requested execution of a stateful DoFn, but no user state context '
            'is available. This likely means that the current runner does not '
            'support the execution of stateful DoFns.')
  
      self.do_fn_invoker = DoFnInvoker.create_invoker(
          do_fn_signature, output_processor, self.context, side_inputs, args,
          kwargs, user_state_context=user_state_context)
  
    def receive(self, windowed_value):
      self.process(windowed_value)
  
    def process(self, windowed_value):
      try:
        self.do_fn_invoker.invoke_process(windowed_value)
      except BaseException as exn:
        self._reraise_augmented(exn)
  
    def process_user_timer(self, timer_spec, key, window, timestamp):
      try:
        self.do_fn_invoker.invoke_user_timer(timer_spec, key, window, timestamp)
      except BaseException as exn:
        self._reraise_augmented(exn)
  
    def _invoke_bundle_method(self, bundle_method):
      try:
        self.context.set_element(None)
        bundle_method()
      except BaseException as exn:
        self._reraise_augmented(exn)
  
    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).
      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
      raise_with_traceback(new_exn)
  
  
  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,
                 per_element_output_counter):
      """Initializes ``_OutputProcessor``.
  
      Args:
        window_fn: a windowing function (WindowFn).
        main_receivers: a dict of tag name to Receiver objects.
        tagged_receivers: main receiver object.
        per_element_output_counter: per_element_output_counter of one work_item.
                                    could be none if experimental flag turn off
      """
      self.window_fn = window_fn
      self.main_receivers = main_receivers
      self.tagged_receivers = tagged_receivers
      self.per_element_output_counter = per_element_output_counter
  
    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:
        # TODO(BEAM-3937): Remove if block after output counter released.
        # Only enable per_element_output_counter when counter cythonized.
        if (self.per_element_output_counter is not None and
            self.per_element_output_counter.is_cythonized):
          self.per_element_output_counter.add_input(0)
        return
  
      output_element_count = 0
      for result in results:
        # results here may be a generator, which cannot call len on it.
        output_element_count += 1
        tag = None
        if isinstance(result, TaggedOutput):
          tag = result.tag
          if not isinstance(tag, (str, unicode)):
            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)
      # TODO(BEAM-3937): Remove if block after output counter released.
      # Only enable per_element_output_counter when counter cythonized
      if (self.per_element_output_counter is not None and
          self.per_element_output_counter.is_cythonized):
        self.per_element_output_counter.add_input(output_element_count)
  
    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, (str, unicode)):
            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
  