#
# 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
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# 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
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#

"""This module contains Splittable DoFn logic that is specific to DirectRunner.
"""

# pytype: skip-file

from __future__ import absolute_import

import uuid
from builtins import object
from threading import Lock
from threading import Timer
from typing import TYPE_CHECKING
from typing import Any
from typing import Iterable
from typing import Optional

import apache_beam as beam
from apache_beam import TimeDomain
from apache_beam import pvalue
from apache_beam.coders import typecoders
from apache_beam.io.iobase import RestrictionTracker
from apache_beam.pipeline import AppliedPTransform
from apache_beam.pipeline import PTransformOverride
from apache_beam.runners.common import DoFnContext
from apache_beam.runners.common import DoFnInvoker
from apache_beam.runners.common import DoFnSignature
from apache_beam.runners.common import OutputProcessor
from apache_beam.runners.direct.evaluation_context import DirectStepContext
from apache_beam.runners.direct.util import KeyedWorkItem
from apache_beam.runners.direct.watermark_manager import WatermarkManager
from apache_beam.runners.sdf_utils import NoOpWatermarkEstimatorProvider
from apache_beam.transforms.core import ParDo
from apache_beam.transforms.core import ProcessContinuation
from apache_beam.transforms.ptransform import PTransform
from apache_beam.transforms.trigger import _ReadModifyWriteStateTag
from apache_beam.utils.windowed_value import WindowedValue

if TYPE_CHECKING:
  from apache_beam.iobase import WatermarkEstimator


class SplittableParDoOverride(PTransformOverride):
  """A transform override for ParDo transformss of SplittableDoFns.

  Replaces the ParDo transform with a SplittableParDo transform that performs
  SDF specific logic.
  """
  def matches(self, applied_ptransform):
    assert isinstance(applied_ptransform, AppliedPTransform)
    transform = applied_ptransform.transform
    if isinstance(transform, ParDo):
      signature = DoFnSignature(transform.fn)
      return signature.is_splittable_dofn()

  def get_replacement_transform(self, ptransform):
    assert isinstance(ptransform, ParDo)
    do_fn = ptransform.fn
    signature = DoFnSignature(do_fn)
    if signature.is_splittable_dofn():
      return SplittableParDo(ptransform)
    else:
      return ptransform


class SplittableParDo(PTransform):
  """A transform that processes a PCollection using a Splittable DoFn."""
  def __init__(self, ptransform):
    assert isinstance(ptransform, ParDo)
    self._ptransform = ptransform

  def expand(self, pcoll):
    sdf = self._ptransform.fn
    signature = DoFnSignature(sdf)
    restriction_coder = signature.get_restriction_coder()
    element_coder = typecoders.registry.get_coder(pcoll.element_type)

    keyed_elements = (
        pcoll
        | 'pair' >> ParDo(PairWithRestrictionFn(sdf))
        | 'split' >> ParDo(SplitRestrictionFn(sdf))
        | 'explode' >> ParDo(ExplodeWindowsFn())
        | 'random' >> ParDo(RandomUniqueKeyFn()))

    return keyed_elements | ProcessKeyedElements(
        sdf,
        element_coder,
        restriction_coder,
        pcoll.windowing,
        self._ptransform.args,
        self._ptransform.kwargs,
        self._ptransform.side_inputs)


class ElementAndRestriction(object):
  """A holder for an element and a restriction."""
  def __init__(self, element, restriction):
    self.element = element
    self.restriction = restriction


class PairWithRestrictionFn(beam.DoFn):
  """A transform that pairs each element with a restriction."""
  def __init__(self, do_fn):
    self._do_fn = do_fn

  def start_bundle(self):
    signature = DoFnSignature(self._do_fn)
    self._invoker = DoFnInvoker.create_invoker(
        signature,
        output_processor=_NoneShallPassOutputProcessor(),
        process_invocation=False)

  def process(self, element, window=beam.DoFn.WindowParam, *args, **kwargs):
    initial_restriction = self._invoker.invoke_initial_restriction(element)
    yield ElementAndRestriction(element, initial_restriction)


class SplitRestrictionFn(beam.DoFn):
  """A transform that perform initial splitting of Splittable DoFn inputs."""
  def __init__(self, do_fn):
    self._do_fn = do_fn

  def start_bundle(self):
    signature = DoFnSignature(self._do_fn)
    self._invoker = DoFnInvoker.create_invoker(
        signature,
        output_processor=_NoneShallPassOutputProcessor(),
        process_invocation=False)

  def process(self, element_and_restriction, *args, **kwargs):
    element = element_and_restriction.element
    restriction = element_and_restriction.restriction
    restriction_parts = self._invoker.invoke_split(element, restriction)
    for part in restriction_parts:
      yield ElementAndRestriction(element, part)


class ExplodeWindowsFn(beam.DoFn):
  """A transform that forces the runner to explode windows.

  This is done to make sure that Splittable DoFn proceses an element for each of
  the windows that element belongs to.
  """
  def process(self, element, window=beam.DoFn.WindowParam, *args, **kwargs):
    yield element


class RandomUniqueKeyFn(beam.DoFn):
  """A transform that assigns a unique key to each element."""
  def process(self, element, window=beam.DoFn.WindowParam, *args, **kwargs):
    # We ignore UUID collisions here since they are extremely rare.
    yield (uuid.uuid4().bytes, element)


class ProcessKeyedElements(PTransform):
  """A primitive transform that performs SplittableDoFn magic.

  Input to this transform should be a PCollection of keyed ElementAndRestriction
  objects.
  """
  def __init__(
      self,
      sdf,
      element_coder,
      restriction_coder,
      windowing_strategy,
      ptransform_args,
      ptransform_kwargs,
      ptransform_side_inputs):
    self.sdf = sdf
    self.element_coder = element_coder
    self.restriction_coder = restriction_coder
    self.windowing_strategy = windowing_strategy
    self.ptransform_args = ptransform_args
    self.ptransform_kwargs = ptransform_kwargs
    self.ptransform_side_inputs = ptransform_side_inputs

  def expand(self, pcoll):
    return pvalue.PCollection.from_(pcoll)


class ProcessKeyedElementsViaKeyedWorkItemsOverride(PTransformOverride):
  """A transform override for ProcessElements transform."""
  def matches(self, applied_ptransform):
    return isinstance(applied_ptransform.transform, ProcessKeyedElements)

  def get_replacement_transform(self, ptransform):
    return ProcessKeyedElementsViaKeyedWorkItems(ptransform)


class ProcessKeyedElementsViaKeyedWorkItems(PTransform):
  """A transform that processes Splittable DoFn input via KeyedWorkItems."""
  def __init__(self, process_keyed_elements_transform):
    self._process_keyed_elements_transform = process_keyed_elements_transform

  def expand(self, pcoll):
    process_elements = ProcessElements(self._process_keyed_elements_transform)
    process_elements.args = (
        self._process_keyed_elements_transform.ptransform_args)
    process_elements.kwargs = (
        self._process_keyed_elements_transform.ptransform_kwargs)
    process_elements.side_inputs = (
        self._process_keyed_elements_transform.ptransform_side_inputs)
    return pcoll | beam.core.GroupByKey() | process_elements


class ProcessElements(PTransform):
  """A primitive transform for processing keyed elements or KeyedWorkItems.

  Will be evaluated by
  `runners.direct.transform_evaluator._ProcessElementsEvaluator`.
  """
  def __init__(self, process_keyed_elements_transform):
    self._process_keyed_elements_transform = process_keyed_elements_transform
    self.sdf = self._process_keyed_elements_transform.sdf

  def expand(self, pcoll):
    return pvalue.PCollection.from_(pcoll)

  def new_process_fn(self, sdf):
    return ProcessFn(
        sdf,
        self._process_keyed_elements_transform.ptransform_args,
        self._process_keyed_elements_transform.ptransform_kwargs)


class ProcessFn(beam.DoFn):
  """A `DoFn` that executes machineary for invoking a Splittable `DoFn`.

  Input to the `ParDo` step that includes a `ProcessFn` will be a `PCollection`
  of `ElementAndRestriction` objects.

  This class is mainly responsible for following.
  (1) setup environment for properly invoking a Splittable `DoFn`.
  (2) invoke `process()` method of a Splittable `DoFn`.
  (3) after the `process()` invocation of the Splittable `DoFn`, determine if a
  re-invocation of the element is needed. If this is the case, set state and
  a timer for a re-invocation and hold output watermark till this
  re-invocation.
  (4) after the final invocation of a given element clear any previous state set
  for re-invoking the element and release the output watermark.
  """
  def __init__(self, sdf, args_for_invoker, kwargs_for_invoker):
    self.sdf = sdf
    self._element_tag = _ReadModifyWriteStateTag('element')
    self._restriction_tag = _ReadModifyWriteStateTag('restriction')
    self.watermark_hold_tag = _ReadModifyWriteStateTag('watermark_hold')
    self._process_element_invoker = None
    self._output_processor = _OutputProcessor()

    self.sdf_invoker = DoFnInvoker.create_invoker(
        DoFnSignature(self.sdf),
        context=DoFnContext('unused_context'),
        output_processor=self._output_processor,
        input_args=args_for_invoker,
        input_kwargs=kwargs_for_invoker)

    self._step_context = None

  @property
  def step_context(self):
    return self._step_context

  @step_context.setter
  def step_context(self, step_context):
    assert isinstance(step_context, DirectStepContext)
    self._step_context = step_context

  def set_process_element_invoker(self, process_element_invoker):
    assert isinstance(process_element_invoker, SDFProcessElementInvoker)
    self._process_element_invoker = process_element_invoker

  def process(
      self,
      element,
      timestamp=beam.DoFn.TimestampParam,
      window=beam.DoFn.WindowParam,
      *args,
      **kwargs):
    if isinstance(element, KeyedWorkItem):
      # Must be a timer firing.
      key = element.encoded_key
    else:
      key, values = element
      values = list(values)
      assert len(values) == 1
      # Value here will either be a WindowedValue or an ElementAndRestriction
      # object.
      # TODO: handle key collisions here.
      assert len(values) == 1, 'Internal error. Processing of splittable ' \
                               'DoFn cannot continue since elements did not ' \
                               'have unique keys.'
      value = values[0]
      if len(values) != 1:
        raise ValueError('')

    state = self._step_context.get_keyed_state(key)
    element_state = state.get_state(window, self._element_tag)
    # Initially element_state is an empty list.
    is_seed_call = not element_state

    if not is_seed_call:
      element = state.get_state(window, self._element_tag)
      restriction = state.get_state(window, self._restriction_tag)
      windowed_element = WindowedValue(element, timestamp, [window])
    else:
      # After values iterator is expanded above we should have gotten a list
      # with a single ElementAndRestriction object.
      assert isinstance(value, ElementAndRestriction)
      element_and_restriction = value
      element = element_and_restriction.element
      restriction = element_and_restriction.restriction

      if isinstance(value, WindowedValue):
        windowed_element = WindowedValue(
            element, value.timestamp, value.windows)
      else:
        windowed_element = WindowedValue(element, timestamp, [window])

    tracker = self.sdf_invoker.invoke_create_tracker(restriction)
    assert self._process_element_invoker
    assert isinstance(self._process_element_invoker, SDFProcessElementInvoker)

    output_values = self._process_element_invoker.invoke_process_element(
        self.sdf_invoker,
        self._output_processor,
        windowed_element,
        tracker,
        *args,
        **kwargs)

    sdf_result = None
    for output in output_values:
      if isinstance(output, SDFProcessElementInvoker.Result):
        # SDFProcessElementInvoker.Result should be the last item yielded.
        sdf_result = output
        break
      yield output

    assert sdf_result, ('SDFProcessElementInvoker must return a '
                        'SDFProcessElementInvoker.Result object as the last '
                        'value of a SDF invoke_process_element() invocation.')

    if not sdf_result.residual_restriction:
      # All work for current residual and restriction pair is complete.
      state.clear_state(window, self._element_tag)
      state.clear_state(window, self._restriction_tag)
      # Releasing output watermark by setting it to positive infinity.
      state.add_state(
          window, self.watermark_hold_tag, WatermarkManager.WATERMARK_POS_INF)
    else:
      state.add_state(window, self._element_tag, element)
      state.add_state(
          window, self._restriction_tag, sdf_result.residual_restriction)
      # Holding output watermark by setting it to negative infinity.
      state.add_state(
          window, self.watermark_hold_tag, WatermarkManager.WATERMARK_NEG_INF)

      # Setting a timer to be reinvoked to continue processing the element.
      # Currently Python SDK only supports setting timers based on watermark. So
      # forcing a reinvocation by setting a timer for watermark negative
      # infinity.
      # TODO(chamikara): update this by setting a timer for the proper
      # processing time when Python SDK supports that.
      state.set_timer(
          window, '', TimeDomain.WATERMARK, WatermarkManager.WATERMARK_NEG_INF)


class SDFProcessElementInvoker(object):
  """A utility that invokes SDF `process()` method and requests checkpoints.

  This class is responsible for invoking the `process()` method of a Splittable
  `DoFn` and making sure that invocation terminated properly. Based on the input
  configuration, this class may decide to request a checkpoint for a `process()`
  execution so that runner can process current output and resume the invocation
  at a later time.

  More specifically, when initializing a `SDFProcessElementInvoker`, caller may
  specify the number of output elements or processing time after which a
  checkpoint should be requested. This class is responsible for properly
  requesting a checkpoint based on either of these criteria.
  When the `process()` call of Splittable `DoFn` ends, this class performs
  validations to make sure that processing ended gracefully and returns a
  `SDFProcessElementInvoker.Result` that contains information which can be used
  by the caller to perform another `process()` invocation for the residual.

  A `process()` invocation may decide to give up processing voluntarily by
  returning a `ProcessContinuation` object (see documentation of
  `ProcessContinuation` for more details). So if a 'ProcessContinuation' is
  produced this class ends the execution and performs steps to finalize the
  current invocation.
  """
  class Result(object):
    def __init__(
        self,
        residual_restriction=None,
        process_continuation=None,
        future_output_watermark=None):
      """Returned as a result of a `invoke_process_element()` invocation.

      Args:
        residual_restriction: a restriction for the unprocessed part of the
                             element.
        process_continuation: a `ProcessContinuation` if one was returned as the
                              last element of the SDF `process()` invocation.
        future_output_watermark: output watermark of the results that will be
                                 produced when invoking the Splittable `DoFn`
                                 for the current element with
                                 `residual_restriction`.
      """

      self.residual_restriction = residual_restriction
      self.process_continuation = process_continuation
      self.future_output_watermark = future_output_watermark

  def __init__(self, max_num_outputs, max_duration):
    self._max_num_outputs = max_num_outputs
    self._max_duration = max_duration
    self._checkpoint_lock = Lock()

  def test_method(self):
    raise ValueError

  def invoke_process_element(
      self, sdf_invoker, output_processor, element, tracker, *args, **kwargs):
    """Invokes `process()` method of a Splittable `DoFn` for a given element.

     Args:
       sdf_invoker: a `DoFnInvoker` for the Splittable `DoFn`.
       element: the element to process
       tracker: a `RestrictionTracker` for the element that will be passed when
                invoking the `process()` method of the Splittable `DoFn`.
     Returns:
       a `SDFProcessElementInvoker.Result` object.
     """
    assert isinstance(sdf_invoker, DoFnInvoker)
    assert isinstance(tracker, RestrictionTracker)

    class CheckpointState(object):
      def __init__(self):
        self.checkpointed = None
        self.residual_restriction = None

    checkpoint_state = CheckpointState()

    def initiate_checkpoint():
      with self._checkpoint_lock:
        if checkpoint_state.checkpointed:
          return
      checkpoint_state.residual_restriction = tracker.checkpoint()
      checkpoint_state.checkpointed = object()

    output_processor.reset()
    noop_estimator = (
        NoOpWatermarkEstimatorProvider().create_watermark_estimator(None))
    Timer(self._max_duration, initiate_checkpoint).start()
    sdf_invoker.invoke_process(
        element,
        restriction_tracker=tracker,
        watermark_estimator=noop_estimator,
        additional_args=args,
        additional_kwargs=kwargs)

    assert output_processor.output_iter is not None
    output_count = 0

    # We have to expand and re-yield here to support ending execution for a
    # given number of output elements as well as to capture the
    # ProcessContinuation of one was returned.
    process_continuation = None
    for output in output_processor.output_iter:
      # A ProcessContinuation, if returned, should be the last element.
      assert not process_continuation
      if isinstance(output, ProcessContinuation):
        # Taking a checkpoint so that we can determine primary and residual
        # restrictions.
        initiate_checkpoint()

        # A ProcessContinuation should always be the last element produced by
        # the output iterator.
        # TODO: support continuing after the specified amount of delay.

        # Continuing here instead of breaking to enforce that this is the last
        # element.
        process_continuation = output
        continue

      yield output
      output_count += 1
      if self._max_num_outputs and output_count >= self._max_num_outputs:
        initiate_checkpoint()

    tracker.check_done()
    result = (
        SDFProcessElementInvoker.Result(
            residual_restriction=checkpoint_state.residual_restriction)
        if checkpoint_state.residual_restriction else
        SDFProcessElementInvoker.Result())
    yield result


class _OutputProcessor(OutputProcessor):
  def __init__(self):
    self.output_iter = None

  def process_outputs(
      self, windowed_input_element, output_iter, watermark_estimator=None):
    # type: (WindowedValue, Iterable[Any], Optional[WatermarkEstimator]) -> None
    self.output_iter = output_iter

  def reset(self):
    self.output_iter = None


class _NoneShallPassOutputProcessor(OutputProcessor):
  def process_outputs(
      self, windowed_input_element, output_iter, watermark_estimator=None):
    # type: (WindowedValue, Iterable[Any], Optional[WatermarkEstimator]) -> None
    raise RuntimeError()
