text:
"""A runner implementation that submits a job for remote execution.

The runner will create a JSON description of the job graph and then submit it
to the Dataflow Service for remote execution by a worker.
"""
# pytype: skip-file

import base64
import logging
import os
import threading
import time
import traceback
import warnings
from collections import defaultdict
from subprocess import DEVNULL
from typing import TYPE_CHECKING
from typing import List
from urllib.parse import quote
from urllib.parse import quote_from_bytes
from urllib.parse import unquote_to_bytes

import apache_beam as beam
from apache_beam import coders
from apache_beam import error
from apache_beam.internal import pickler
from apache_beam.internal.gcp import json_value
from apache_beam.options.pipeline_options import DebugOptions
from apache_beam.options.pipeline_options import GoogleCloudOptions
from apache_beam.options.pipeline_options import SetupOptions
from apache_beam.options.pipeline_options import StandardOptions
from apache_beam.options.pipeline_options import TestOptions
from apache_beam.options.pipeline_options import TypeOptions
from apache_beam.options.pipeline_options import WorkerOptions
from apache_beam.portability import common_urns
from apache_beam.portability.api import beam_runner_api_pb2
from apache_beam.pvalue import AsSideInput
from apache_beam.runners.common import DoFnSignature
from apache_beam.runners.common import group_by_key_input_visitor
from apache_beam.runners.dataflow.internal import names
from apache_beam.runners.dataflow.internal.clients import dataflow as dataflow_api
from apache_beam.runners.dataflow.internal.names import PropertyNames
from apache_beam.runners.dataflow.internal.names import TransformNames
from apache_beam.runners.runner import PipelineResult
from apache_beam.runners.runner import PipelineRunner
from apache_beam.runners.runner import PipelineState
from apache_beam.runners.runner import PValueCache
from apache_beam.transforms import window
from apache_beam.transforms.display import DisplayData
from apache_beam.transforms.sideinputs import SIDE_INPUT_PREFIX
from apache_beam.typehints import typehints
from apache_beam.utils import processes
from apache_beam.utils import proto_utils
from apache_beam.utils.interactive_utils import is_in_notebook
from apache_beam.utils.plugin import BeamPlugin

if TYPE_CHECKING:
  from apache_beam.pipeline import PTransformOverride

__all__ = ['DataflowRunner']

_LOGGER = logging.getLogger(__name__)

BQ_SOURCE_UW_ERROR = (
    'The Read(BigQuerySource(...)) transform is not supported with newer stack '
    'features (Fn API, Dataflow Runner V2, etc). Please use the transform '
    'apache_beam.io.gcp.bigquery.ReadFromBigQuery instead.')

class DataflowRunner(PipelineRunner):
  """A runner that creates job graphs and submits them for remote execution.

  Every execution of the run() method will submit an independent job for
  remote execution that consists of the nodes reachable from the passed in
  node argument or entire graph if node is None. The run() method returns
  after the service created the job and  will not wait for the job to finish
  if blocking is set to False.
  """

  # A list of PTransformOverride objects to be applied before running a pipeline
  # using DataflowRunner.
  # Currently this only works for overrides where the input and output types do
  # not change.
  # For internal SDK use only. This should not be updated by Beam pipeline
  # authors.

  # Imported here to avoid circular dependencies.
  # TODO: Remove the apache_beam.pipeline dependency in CreatePTransformOverride
  from apache_beam.runners.dataflow.ptransform_overrides import CombineValuesPTransformOverride
  from apache_beam.runners.dataflow.ptransform_overrides import CreatePTransformOverride
  from apache_beam.runners.dataflow.ptransform_overrides import JrhReadPTransformOverride
  from apache_beam.runners.dataflow.ptransform_overrides import ReadPTransformOverride
  from apache_beam.runners.dataflow.ptransform_overrides import NativeReadPTransformOverride

  # These overrides should be applied before the proto representation of the
  # graph is created.
  _PTRANSFORM_OVERRIDES = [
      CombineValuesPTransformOverride(),
      NativeReadPTransformOverride(),
  ]  # type: List[PTransformOverride]

  _JRH_PTRANSFORM_OVERRIDES = [
      JrhReadPTransformOverride(),
  ]  # type: List[PTransformOverride]

  # These overrides should be applied after the proto representation of the
  # graph is created.
  _NON_PORTABLE_PTRANSFORM_OVERRIDES = [
      CreatePTransformOverride(),
      ReadPTransformOverride(),
  ]  # type: List[PTransformOverride]

  def __init__(self, cache=None):
    # Cache of CloudWorkflowStep protos generated while the runner
    # "executes" a pipeline.
    self._cache = cache if cache is not None else PValueCache()
    self._unique_step_id = 0
    self._default_environment = None

  def is_fnapi_compatible(self):
    return False

  def apply(self, transform, input, options):
    self._maybe_add_unified_worker_missing_options(options)
    return super().apply(transform, input, options)

  def _get_unique_step_name(self):
    self._unique_step_id += 1
    return 's%s' % self._unique_step_id

  @staticmethod
  def poll_for_job_completion(
      runner, result, duration, state_update_callback=None):
    """Polls for the specified job to finish running (successfully or not).

    Updates the result with the new job information before returning.

    Args:
      runner: DataflowRunner instance to use for polling job state.
      result: DataflowPipelineResult instance used for job information.
      duration (int): The time to wait (in milliseconds) for job to finish.
        If it is set to :data:`None`, it will wait indefinitely until the job
        is finished.
    """
    if result.state == PipelineState.DONE:
      return

    last_message_time = None
    current_seen_messages = set()

    last_error_rank = float('-inf')
    last_error_msg = None
    last_job_state = None
    # How long to wait after pipeline failure for the error
    # message to show up giving the reason for the failure.
    # It typically takes about 30 seconds.
    final_countdown_timer_secs = 50.0
    sleep_secs = 5.0

    # Try to prioritize the user-level traceback, if any.
    def rank_error(msg):
      if 'work item was attempted' in msg:
        return -1
      elif 'Traceback' in msg:
        return 1
      return 0

    if duration:
      start_secs = time.time()
      duration_secs = duration // 1000

    job_id = result.job_id()
    while True:
      response = runner.dataflow_client.get_job(job_id)
      # If get() is called very soon after Create() the response may not contain
      # an initialized 'currentState' field.
      if response.currentState is not None:
        if response.currentState != last_job_state:
          if state_update_callback:
            state_update_callback(response.currentState)
          _LOGGER.info('Job %s is in state %s', job_id, response.currentState)
          last_job_state = response.currentState
        if str(response.currentState) != 'JOB_STATE_RUNNING':
          # Stop checking for new messages on timeout, explanatory
          # message received, success, or a terminal job state caused
          # by the user that therefore doesn't require explanation.
          if (final_countdown_timer_secs <= 0.0 or last_error_msg is not None or
              str(response.currentState) == 'JOB_STATE_DONE' or
              str(response.currentState) == 'JOB_STATE_CANCELLED' or
              str(response.currentState) == 'JOB_STATE_UPDATED' or
              str(response.currentState) == 'JOB_STATE_DRAINED'):
            break

          # Check that job is in a post-preparation state before starting the
          # final countdown.
          if (str(response.currentState) not in ('JOB_STATE_PENDING',
                                                 'JOB_STATE_QUEUED')):
            # The job has failed; ensure we see any final error messages.
            sleep_secs = 1.0  # poll faster during the final countdown
            final_countdown_timer_secs -= sleep_secs

      time.sleep(sleep_secs)

      # Get all messages since beginning of the job run or since last message.
      page_token = None
      while True:
        messages, page_token = runner.dataflow_client.list_messages(
            job_id, page_token=page_token, start_time=last_message_time)
        for m in messages:
          message = '%s: %s: %s' % (m.time, m.messageImportance, m.messageText)

          if not last_message_time or m.time > last_message_time:
            last_message_time = m.time
            current_seen_messages = set()

          if message in current_seen_messages:
            # Skip the message if it has already been seen at the current
            # time. This could be the case since the list_messages API is
            # queried starting at last_message_time.
            continue
          else:
            current_seen_messages.add(message)
          # Skip empty messages.
          if m.messageImportance is None:
            continue
          _LOGGER.info(message)
          if str(m.messageImportance) == 'JOB_MESSAGE_ERROR':
            if rank_error(m.messageText) >= last_error_rank:
              last_error_rank = rank_error(m.messageText)
              last_error_msg = m.messageText
        if not page_token:
          break

      if duration:
        passed_secs = time.time() - start_secs
        if passed_secs > duration_secs:
          _LOGGER.warning(
              'Timing out on waiting for job %s after %d seconds',
              job_id,
              passed_secs)
          break

    result._job = response
    runner.last_error_msg = last_error_msg

  @staticmethod
  def _only_element(iterable):
    # type: (Iterable[T]) -> T # noqa: F821
    element, = iterable
    return element

  @staticmethod
  def side_input_visitor(
      use_unified_worker=False,
      use_fn_api=False,
      deterministic_key_coders=True):
    # Imported here to avoid circular dependencies.
    # pylint: disable=wrong-import-order, wrong-import-position
    from apache_beam.pipeline import PipelineVisitor
    from apache_beam.transforms.core import ParDo

    class SideInputVisitor(PipelineVisitor):
      """Ensures input `PCollection` used as a side inputs has a `KV` type.

      TODO(BEAM-115): Once Python SDK is compatible with the new Runner API,
      we could directly replace the coder instead of mutating the element type.
      """
      def visit_transform(self, transform_node):
        if isinstance(transform_node.transform, ParDo):
          new_side_inputs = []
          for ix, side_input in enumerate(transform_node.side_inputs):
            access_pattern = side_input._side_input_data().access_pattern
            if access_pattern == common_urns.side_inputs.ITERABLE.urn:
              if use_unified_worker or not use_fn_api:
                # TODO(https://github.com/apache/beam/issues/20043): Stop
                # patching up the access pattern to appease Dataflow when
                # using the UW and hardcode the output type to be Any since
                # the Dataflow JSON and pipeline proto can differ in coders
                # which leads to encoding/decoding issues within the runner.
                side_input.pvalue.element_type = typehints.Any
                new_side_input = _DataflowIterableSideInput(side_input)
              else:
                # Add a map to ('', value) as Dataflow currently only handles
                # keyed side inputs when using the JRH.
                pipeline = side_input.pvalue.pipeline
                new_side_input = _DataflowIterableAsMultimapSideInput(
                    side_input)
                new_side_input.pvalue = beam.pvalue.PCollection(
                    pipeline,
                    element_type=typehints.KV[bytes,
                                              side_input.pvalue.element_type],
                    is_bounded=side_input.pvalue.is_bounded)
                parent = transform_node.parent or pipeline._root_transform()
                map_to_void_key = beam.pipeline.AppliedPTransform(
                    parent,
                    beam.Map(lambda x: (b'', x)),
                    transform_node.full_label + '/MapToVoidKey%s' % ix,
                    {'input': side_input.pvalue})
                new_side_input.pvalue.producer = map_to_void_key
                map_to_void_key.add_output(new_side_input.pvalue, None)
                parent.add_part(map_to_void_key)
            elif access_pattern == common_urns.side_inputs.MULTIMAP.urn:
              # Ensure the input coder is a KV coder and patch up the
              # access pattern to appease Dataflow.
              side_input.pvalue.element_type = typehints.coerce_to_kv_type(
                  side_input.pvalue.element_type, transform_node.full_label)
              side_input.pvalue.requires_deterministic_key_coder = (
                  deterministic_key_coders and transform_node.full_label)
              new_side_input = _DataflowMultimapSideInput(side_input)
            else:
              raise ValueError(
                  'Unsupported access pattern for %r: %r' %
                  (transform_node.full_label, access_pattern))
            new_side_inputs.append(new_side_input)
          if use_fn_api:
            transform_node.side_inputs = new_side_inputs
            transform_node.transform.side_inputs = new_side_inputs

    return SideInputVisitor()

  @staticmethod
  def flatten_input_visitor():
    # Imported here to avoid circular dependencies.
    from apache_beam.pipeline import PipelineVisitor

    class FlattenInputVisitor(PipelineVisitor):
      """A visitor that replaces the element type for input ``PCollections``s of
       a ``Flatten`` transform with that of the output ``PCollection``.
      """
      def visit_transform(self, transform_node):
        # Imported here to avoid circular dependencies.
        # pylint: disable=wrong-import-order, wrong-import-position
        from apache_beam import Flatten
        if isinstance(transform_node.transform, Flatten):
          output_pcoll = DataflowRunner._only_element(
              transform_node.outputs.values())
          for input_pcoll in transform_node.inputs:
            input_pcoll.element_type = output_pcoll.element_type

    return FlattenInputVisitor()

  @staticmethod
  def combinefn_visitor():
    # Imported here to avoid circular dependencies.
    from apache_beam.pipeline import PipelineVisitor
    from apache_beam import core

    class CombineFnVisitor(PipelineVisitor):
      """Checks if `CombineFn` has non-default setup or teardown methods.
      If yes, raises `ValueError`.
      """
      def visit_transform(self, applied_transform):
        transform = applied_transform.transform
        if isinstance(transform, core.ParDo) and isinstance(
            transform.fn, core.CombineValuesDoFn):
          if self._overrides_setup_or_teardown(transform.fn.combinefn):
            raise ValueError(
                'CombineFn.setup and CombineFn.teardown are '
                'not supported with non-portable Dataflow '
                'runner. Please use Dataflow Runner V2 instead.')

      @staticmethod
      def _overrides_setup_or_teardown(combinefn):
        # TODO(https://github.com/apache/beam/issues/18716): provide an
        # implementation for this method
        return False

    return CombineFnVisitor()

  def _adjust_pipeline_for_dataflow_v2(self, pipeline):
    # Dataflow runner requires a KV type for GBK inputs, hence we enforce that
    # here.
    pipeline.visit(
        group_by_key_input_visitor(
            not pipeline._options.view_as(
                TypeOptions).allow_non_deterministic_key_coders))

  def _check_for_unsupported_features_on_non_portable_worker(self, pipeline):
    pipeline.visit(self.combinefn_visitor())

  def run_pipeline(self, pipeline, options, pipeline_proto=None):
    """Remotely executes entire pipeline or parts reachable from node."""
    # Label goog-dataflow-notebook if job is started from notebook.
    if is_in_notebook():
      notebook_version = (
          'goog-dataflow-notebook=' +
          beam.version.__version__.replace('.', '_'))
      if options.view_as(GoogleCloudOptions).labels:
        options.view_as(GoogleCloudOptions).labels.append(notebook_version)
      else:
        options.view_as(GoogleCloudOptions).labels = [notebook_version]

    # Import here to avoid adding the dependency for local running scenarios.
    try:
      # pylint: disable=wrong-import-order, wrong-import-position
      from apache_beam.runners.dataflow.internal import apiclient
    except ImportError:
      raise ImportError(
          'Google Cloud Dataflow runner not available, '
          'please install apache_beam[gcp]')

    debug_options = options.view_as(DebugOptions)
    if pipeline_proto or pipeline.contains_external_transforms:
      if debug_options.lookup_experiment('disable_runner_v2'):
        raise ValueError(
            'This pipeline contains cross language transforms, '
            'which require runner v2.')
      if not apiclient._use_unified_worker(options):
        _LOGGER.info(
            'Automatically enabling Dataflow Runner v2 since the '
            'pipeline used cross-language transforms.')
        # This has to be done before any Fn API specific setup.
        debug_options.add_experiment("use_runner_v2")
      # Dataflow multi-language pipelines require portable job submission.
      if not debug_options.lookup_experiment('use_portable_job_submission'):
        debug_options.add_experiment("use_portable_job_submission")

    self._maybe_add_unified_worker_missing_options(options)

    use_fnapi = apiclient._use_fnapi(options)

    if not use_fnapi:
      self._check_for_unsupported_features_on_non_portable_worker(pipeline)

    # Convert all side inputs into a form acceptable to Dataflow.
    if pipeline:
      pipeline.visit(
          self.side_input_visitor(
              apiclient._use_unified_worker(options),
              apiclient._use_fnapi(options),
              deterministic_key_coders=not options.view_as(
                  TypeOptions).allow_non_deterministic_key_coders))

      # Performing configured PTransform overrides.  Note that this is currently
      # done before Runner API serialization, since the new proto needs to
      # contain any added PTransforms.
      pipeline.replace_all(DataflowRunner._PTRANSFORM_OVERRIDES)

      if debug_options.lookup_experiment('use_legacy_bq_sink'):
        warnings.warn(
            "Native sinks no longer implemented; "
            "ignoring use_legacy_bq_sink.")

      from apache_beam.runners.dataflow.ptransform_overrides import GroupIntoBatchesWithShardedKeyPTransformOverride
      pipeline.replace_all(
          [GroupIntoBatchesWithShardedKeyPTransformOverride(self, options)])

      if use_fnapi and not apiclient._use_unified_worker(options):
        pipeline.replace_all(DataflowRunner._JRH_PTRANSFORM_OVERRIDES)

    if pipeline_proto:
      self.proto_pipeline = pipeline_proto

    else:
      from apache_beam.transforms import environments
      if options.view_as(SetupOptions).prebuild_sdk_container_engine:
        # if prebuild_sdk_container_engine is specified we will build a new sdk
        # container image with dependencies pre-installed and use that image,
        # instead of using the inferred default container image.
        self._default_environment = (
            environments.DockerEnvironment.from_options(options))
        options.view_as(WorkerOptions).sdk_container_image = (
            self._default_environment.container_image)
      else:
        artifacts = environments.python_sdk_dependencies(options)
        if artifacts and apiclient._use_fnapi(options):
          _LOGGER.info(
              "Pipeline has additional dependencies to be installed "
              "in SDK worker container, consider using the SDK "
              "container image pre-building workflow to avoid "
              "repetitive installations. Learn more on "
              "https://cloud.google.com/dataflow/docs/guides/"
              "using-custom-containers#prebuild")
        self._default_environment = (
            environments.DockerEnvironment.from_container_image(
                apiclient.get_container_image_from_options(options),
                artifacts=artifacts,
                resource_hints=environments.resource_hints_from_options(
                    options)))

      # This has to be performed before pipeline proto is constructed to make
      # sure that the changes are reflected in the portable job submission path.
      self._adjust_pipeline_for_dataflow_v2(pipeline)

      # Snapshot the pipeline in a portable proto.
      self.proto_pipeline, self.proto_context = pipeline.to_runner_api(
          return_context=True, default_environment=self._default_environment)

    # Optimize the pipeline if it not streaming and the pre_optimize
    # experiment is set.
    if not options.view_as(StandardOptions).streaming:
      pre_optimize = options.view_as(DebugOptions).lookup_experiment(
          'pre_optimize', 'default').lower()
      from apache_beam.runners.portability.fn_api_runner import translations
      if pre_optimize == 'none':
        phases = []
      elif pre_optimize == 'default' or pre_optimize == 'all':
        phases = [translations.pack_combiners, translations.sort_stages]
      else:
        phases = []
        for phase_name in pre_optimize.split(','):
          # For now, these are all we allow.
          if phase_name in ('pack_combiners', ):
            phases.append(getattr(translations, phase_name))
          else:
            raise ValueError(
                'Unknown or inapplicable phase for pre_optimize: %s' %
                phase_name)
        phases.append(translations.sort_stages)

      if phases:
        self.proto_pipeline = translations.optimize_pipeline(
            self.proto_pipeline,
            phases=phases,
            known_runner_urns=frozenset(),
            partial=True)

    if not use_fnapi:
      # Performing configured PTransform overrides which should not be reflected
      # in the proto representation of the graph.
      pipeline.replace_all(DataflowRunner._NON_PORTABLE_PTRANSFORM_OVERRIDES)

    # Add setup_options for all the BeamPlugin imports
    setup_options = options.view_as(SetupOptions)
    plugins = BeamPlugin.get_all_plugin_paths()
    if setup_options.beam_plugins is not None:
      plugins = list(set(plugins + setup_options.beam_plugins))
    setup_options.beam_plugins = plugins

    # Elevate "min_cpu_platform" to pipeline option, but using the existing
    # experiment.
    debug_options = options.view_as(DebugOptions)
    worker_options = options.view_as(WorkerOptions)
    if worker_options.min_cpu_platform:
      debug_options.add_experiment(
          'min_cpu_platform=' + worker_options.min_cpu_platform)

    # Elevate "enable_streaming_engine" to pipeline option, but using the
    # existing experiment.
    google_cloud_options = options.view_as(GoogleCloudOptions)
    if google_cloud_options.enable_streaming_engine:
      debug_options.add_experiment("enable_windmill_service")
      debug_options.add_experiment("enable_streaming_engine")
    elif (apiclient._use_fnapi(options) and
          apiclient._use_unified_worker(options) and
          options.view_as(StandardOptions).streaming):
      debug_options.add_experiment("enable_windmill_service")
      debug_options.add_experiment("enable_streaming_engine")
    else:
      if (debug_options.lookup_experiment("enable_windmill_service") or
          debug_options.lookup_experiment("enable_streaming_engine")):
        raise ValueError(
            """Streaming engine both disabled and enabled:
        --enable_streaming_engine flag is not set, but
        enable_windmill_service
        and/or enable_streaming_engine experiments are present.
        It is recommended you only set the --enable_streaming_engine flag.""")

    dataflow_worker_jar = getattr(worker_options, 'dataflow_worker_jar', None)
    if dataflow_worker_jar is not None:
      if not apiclient._use_fnapi(options):
        _LOGGER.warning(
            'Typical end users should not use this worker jar feature. '
            'It can only be used when FnAPI is enabled.')
      else:
        debug_options.add_experiment('use_staged_dataflow_worker_jar')

    self.job = apiclient.Job(options, self.proto_pipeline)

    # TODO: Consider skipping these for all use_portable_job_submission jobs.
    if pipeline:
      # Dataflow Runner v1 requires output type of the Flatten to be the same as
      # the inputs, hence we enforce that here. Dataflow Runner v2 does not
      # require this.
      pipeline.visit(self.flatten_input_visitor())

      # Trigger a traversal of all reachable nodes.
      self.visit_transforms(pipeline, options)

    test_options = options.view_as(TestOptions)
    # If it is a dry run, return without submitting the job.
    if test_options.dry_run:
      result = PipelineResult(PipelineState.DONE)
      result.wait_until_finish = lambda duration=None: None
      return result

    # Get a Dataflow API client and set its options
    self.dataflow_client = apiclient.DataflowApplicationClient(
        options, self.job.root_staging_location)

    # Create the job description and send a request to the service. The result
    # can be None if there is no need to send a request to the service (e.g.
    # template creation). If a request was sent and failed then the call will
    # raise an exception.
    result = DataflowPipelineResult(
        self.dataflow_client.create_job(self.job), self)

    # TODO(BEAM-4274): Circular import runners-metrics. Requires refactoring.
    from apache_beam.runners.dataflow.dataflow_metrics import DataflowMetrics
    self._metrics = DataflowMetrics(self.dataflow_client, result, self.job)
    result.metric_results = self._metrics
    return result

  def _maybe_add_unified_worker_missing_options(self, options):
    debug_options = options.view_as(DebugOptions)
    # Streaming is always portable, default to runner v2.
    if options.view_as(StandardOptions).streaming:
      if debug_options.lookup_experiment('disable_runner_v2_until_2023'):
        debug_options.add_experiment('disable_runner_v2')
      elif debug_options.lookup_experiment('disable_runner_v2'):
        raise ValueError(
            'disable_runner_v2 no longer supported for Beam Python %s, please '
            'use disable_runner_v2_until_2023' % beam.version.__version__)
      else:
        debug_options.add_experiment('beam_fn_api')
        debug_options.add_experiment('use_runner_v2')
        debug_options.add_experiment('use_portable_job_submission')
    # set default beam_fn_api experiment if use unified
    # worker experiment flag exists, no-op otherwise.
    from apache_beam.runners.dataflow.internal import apiclient
    if apiclient._use_unified_worker(options):
      if not debug_options.lookup_experiment('beam_fn_api'):
        debug_options.add_experiment('beam_fn_api')

  def _get_typehint_based_encoding(self, typehint, window_coder):
    """Returns an encoding based on a typehint object."""
    return self._get_cloud_encoding(
        self._get_coder(typehint, window_coder=window_coder))

  @staticmethod
  def _get_coder(typehint, window_coder):
    """Returns a coder based on a typehint object."""
    if window_coder:
      return coders.WindowedValueCoder(
          coders.registry.get_coder(typehint), window_coder=window_coder)
    return coders.registry.get_coder(typehint)

  def _get_cloud_encoding(self, coder, unused=None):
    """Returns an encoding based on a coder object."""
    if not isinstance(coder, coders.Coder):
      raise TypeError(
          'Coder object must inherit from coders.Coder: %s.' % str(coder))
    return coder.as_cloud_object(self.proto_context.coders)

  def _get_side_input_encoding(self, input_encoding):
    """Returns an encoding for the output of a view transform.

    Args:
      input_encoding: encoding of current transform's input. Side inputs need
        this because the service will check that input and output types match.

    Returns:
      An encoding that matches the output and input encoding. This is essential
      for the View transforms introduced to produce side inputs to a ParDo.
    """
    return {
        '@type': 'kind:stream',
        'component_encodings': [input_encoding],
        'is_stream_like': {
            'value': True
        },
    }

  def _get_encoded_output_coder(
      self, transform_node, window_value=True, output_tag=None):
    """Returns the cloud encoding of the coder for the output of a transform."""

    if output_tag in transform_node.outputs:
      element_type = transform_node.outputs[output_tag].element_type
    elif len(transform_node.outputs) == 1:
      output_tag = DataflowRunner._only_element(transform_node.outputs.keys())
      # TODO(robertwb): Handle type hints for multi-output transforms.
      element_type = transform_node.outputs[output_tag].element_type

    else:
      # TODO(silviuc): Remove this branch (and assert) when typehints are
      # propagated everywhere. Returning an 'Any' as type hint will trigger
      # usage of the fallback coder (i.e., cPickler).
      element_type = typehints.Any
    if window_value:
      # All outputs have the same windowing. So getting the coder from an
      # arbitrary window is fine.
      output_tag = next(iter(transform_node.outputs.keys()))
      window_coder = (
          transform_node.outputs[output_tag].windowing.windowfn.
          get_window_coder())
    else:
      window_coder = None
    return self._get_typehint_based_encoding(element_type, window_coder)

  def get_pcoll_with_auto_sharding(self):
    if not hasattr(self, '_pcoll_with_auto_sharding'):
      return set()
    return self._pcoll_with_auto_sharding

  def add_pcoll_with_auto_sharding(self, applied_ptransform):
    if not hasattr(self, '_pcoll_with_auto_sharding'):
      self.__setattr__('_pcoll_with_auto_sharding', set())
    output = DataflowRunner._only_element(applied_ptransform.outputs.keys())
    self._pcoll_with_auto_sharding.add(
        applied_ptransform.outputs[output]._unique_name())

  def _add_step(self, step_kind, step_label, transform_node, side_tags=()):
    """Creates a Step object and adds it to the cache."""
    # Import here to avoid adding the dependency for local running scenarios.
    # pylint: disable=wrong-import-order, wrong-import-position
    from apache_beam.runners.dataflow.internal import apiclient
    step = apiclient.Step(step_kind, self._get_unique_step_name())
    self.job.proto.steps.append(step.proto)
    step.add_property(PropertyNames.USER_NAME, step_label)
    # Cache the node/step association for the main output of the transform node.

    # External transforms may not use 'None' as an output tag.
    output_tags = ([None] +
                   list(side_tags) if None in transform_node.outputs.keys() else
                   list(transform_node.outputs.keys()))

    # We have to cache output for all tags since some transforms may produce
    # multiple outputs.
    for output_tag in output_tags:
      self._cache.cache_output(transform_node, output_tag, step)

    # Finally, we add the display data items to the pipeline step.
    # If the transform contains no display data then an empty list is added.
    step.add_property(
        PropertyNames.DISPLAY_DATA,
        [
            item.get_dict()
            for item in DisplayData.create_from(transform_node.transform).items
        ])

    if transform_node.resource_hints:
      step.add_property(
          PropertyNames.RESOURCE_HINTS,
          {
              hint: quote_from_bytes(value)
              for (hint, value) in transform_node.resource_hints.items()
          })

    return step

  def _add_singleton_step(
      self,
      label,
      full_label,
      tag,
      input_step,
      windowing_strategy,
      access_pattern):
    """Creates a CollectionToSingleton step used to handle ParDo side inputs."""
    # Import here to avoid adding the dependency for local running scenarios.
    from apache_beam.runners.dataflow.internal import apiclient
    step = apiclient.Step(TransformNames.COLLECTION_TO_SINGLETON, label)
    self.job.proto.steps.append(step.proto)
    step.add_property(PropertyNames.USER_NAME, full_label)
    step.add_property(
        PropertyNames.PARALLEL_INPUT,
        {
            '@type': 'OutputReference',
            PropertyNames.STEP_NAME: input_step.proto.name,
            PropertyNames.OUTPUT_NAME: input_step.get_output(tag)
        })
    step.encoding = self._get_side_input_encoding(input_step.encoding)

    output_info = {
        PropertyNames.USER_NAME: '%s.%s' % (full_label, PropertyNames.OUTPUT),
        PropertyNames.ENCODING: step.encoding,
        PropertyNames.OUTPUT_NAME: PropertyNames.OUT
    }
    if common_urns.side_inputs.MULTIMAP.urn == access_pattern:
      output_info[PropertyNames.USE_INDEXED_FORMAT] = True
    step.add_property(PropertyNames.OUTPUT_INFO, [output_info])

    step.add_property(
        PropertyNames.WINDOWING_STRATEGY,
        self.serialize_windowing_strategy(
            windowing_strategy, self._default_environment))
    return step

  def run_Impulse(self, transform_node, options):
    standard_options = options.view_as(StandardOptions)
    debug_options = options.view_as(DebugOptions)
    use_fn_api = (
        debug_options.experiments and
        'beam_fn_api' in debug_options.experiments)
    use_streaming_engine = (
        debug_options.experiments and
        'enable_streaming_engine' in debug_options.experiments and
        'enable_windmill_service' in debug_options.experiments)

    step = self._add_step(
        TransformNames.READ, transform_node.full_label, transform_node)
    if (standard_options.streaming and
        (not use_fn_api or not use_streaming_engine)):
      step.add_property(PropertyNames.FORMAT, 'pubsub')
      step.add_property(PropertyNames.PUBSUB_SUBSCRIPTION, '_starting_signal/')
    else:
      step.add_property(PropertyNames.FORMAT, 'impulse')
      encoded_impulse_element = coders.WindowedValueCoder(
          coders.BytesCoder(),
          coders.coders.GlobalWindowCoder()).get_impl().encode_nested(
              window.GlobalWindows.windowed_value(b''))

      if use_fn_api:
        encoded_impulse_as_str = self.byte_array_to_json_string(
            encoded_impulse_element)
      else:
        encoded_impulse_as_str = base64.b64encode(
            encoded_impulse_element).decode('ascii')
      step.add_property(PropertyNames.IMPULSE_ELEMENT, encoded_impulse_as_str)

    step.encoding = self._get_encoded_output_coder(transform_node)
    step.add_property(
        PropertyNames.OUTPUT_INFO,
        [{
            PropertyNames.USER_NAME: (
                '%s.%s' % (transform_node.full_label, PropertyNames.OUT)),
            PropertyNames.ENCODING: step.encoding,
            PropertyNames.OUTPUT_NAME: PropertyNames.OUT
        }])

  def run_Flatten(self, transform_node, options):
    step = self._add_step(
        TransformNames.FLATTEN, transform_node.full_label, transform_node)
    inputs = []
    for one_input in transform_node.inputs:
      input_step = self._cache.get_pvalue(one_input)
      inputs.append({
          '@type': 'OutputReference',
          PropertyNames.STEP_NAME: input_step.proto.name,
          PropertyNames.OUTPUT_NAME: input_step.get_output(one_input.tag)
      })
    step.add_property(PropertyNames.INPUTS, inputs)
    step.encoding = self._get_encoded_output_coder(transform_node)
    step.add_property(
        PropertyNames.OUTPUT_INFO,
        [{
            PropertyNames.USER_NAME: (
                '%s.%s' % (transform_node.full_label, PropertyNames.OUT)),
            PropertyNames.ENCODING: step.encoding,
            PropertyNames.OUTPUT_NAME: PropertyNames.OUT
        }])

  # TODO(srohde): Remove this after internal usages have been removed.
  def apply_GroupByKey(self, transform, pcoll, options):
    return transform.expand(pcoll)

  def _verify_gbk_coders(self, transform, pcoll):
    # Infer coder of parent.
    #
    # TODO(ccy): make Coder inference and checking less specialized and more
    # comprehensive.

    parent = pcoll.producer
    if parent:
      coder = parent.transform._infer_output_coder()  # pylint: disable=protected-access
    if not coder:
      coder = self._get_coder(pcoll.element_type or typehints.Any, None)
    if not coder.is_kv_coder():
      raise ValueError((
          'Coder for the GroupByKey operation "%s" is not a '
          'key-value coder: %s.') % (transform.label, coder))
    # TODO(robertwb): Update the coder itself if it changed.
    coders.registry.verify_deterministic(
        coder.key_coder(), 'GroupByKey operation "%s"' % transform.label)

  def run_GroupByKey(self, transform_node, options):
    input_tag = transform_node.inputs[0].tag
    input_step = self._cache.get_pvalue(transform_node.inputs[0])

    # Verify that the GBK's parent has a KV coder.
    self._verify_gbk_coders(transform_node.transform, transform_node.inputs[0])

    step = self._add_step(
        TransformNames.GROUP, transform_node.full_label, transform_node)
    step.add_property(
        PropertyNames.PARALLEL_INPUT,
        {
            '@type': 'OutputReference',
            PropertyNames.STEP_NAME: input_step.proto.name,
            PropertyNames.OUTPUT_NAME: input_step.get_output(input_tag)
        })
    step.encoding = self._get_encoded_output_coder(transform_node)
    step.add_property(
        PropertyNames.OUTPUT_INFO,
        [{
            PropertyNames.USER_NAME: (
                '%s.%s' % (transform_node.full_label, PropertyNames.OUT)),
            PropertyNames.ENCODING: step.encoding,
            PropertyNames.OUTPUT_NAME: PropertyNames.OUT
        }])
    windowing = transform_node.transform.get_windowing(transform_node.inputs)
    step.add_property(
        PropertyNames.SERIALIZED_FN,
        self.serialize_windowing_strategy(windowing, self._default_environment))

  def run_ExternalTransform(self, transform_node, options):
    # Adds a dummy step to the Dataflow job description so that inputs and
    # outputs are mapped correctly in the presence of external transforms.
    #
    # Note that Dataflow Python multi-language pipelines use Portable Job
    # Submission by default, hence this step and rest of the Dataflow step
    # definitions defined here are not used at Dataflow service but we have to
    # maintain the mapping correctly till we can fully drop the Dataflow step
    # definitions from the SDK.

    # AppliedTransform node outputs have to be updated to correctly map the
    # outputs for external transforms.
    transform_node.outputs = ({
        output.tag: output
        for output in transform_node.outputs.values()
    })

    self.run_Impulse(transform_node, options)

  def run_ParDo(self, transform_node, options):
    transform = transform_node.transform
    input_tag = transform_node.inputs[0].tag
    input_step = self._cache.get_pvalue(transform_node.inputs[0])

    # Attach side inputs.
    si_dict = {}
    si_labels = {}
    full_label_counts = defaultdict(int)
    lookup_label = lambda side_pval: si_labels[side_pval]
    named_inputs = transform_node.named_inputs()
    label_renames = {}
    for ix, side_pval in enumerate(transform_node.side_inputs):
      assert isinstance(side_pval, AsSideInput)
      step_name = 'SideInput-' + self._get_unique_step_name()
      si_label = ((SIDE_INPUT_PREFIX + '%d-%s') %
                  (ix, transform_node.full_label))
      old_label = (SIDE_INPUT_PREFIX + '%d') % ix

      label_renames[old_label] = si_label

      assert old_label in named_inputs
      pcollection_label = '%s.%s' % (
          side_pval.pvalue.producer.full_label.split('/')[-1],
          side_pval.pvalue.tag if side_pval.pvalue.tag else 'out')
      si_full_label = '%s/%s(%s.%s)' % (
          transform_node.full_label,
          side_pval.__class__.__name__,
          pcollection_label,
          full_label_counts[pcollection_label])

      # Count the number of times the same PCollection is a side input
      # to the same ParDo.
      full_label_counts[pcollection_label] += 1

      self._add_singleton_step(
          step_name,
          si_full_label,
          side_pval.pvalue.tag,
          self._cache.get_pvalue(side_pval.pvalue),
          side_pval.pvalue.windowing,
          side_pval._side_input_data().access_pattern)
      si_dict[si_label] = {
          '@type': 'OutputReference',
          PropertyNames.STEP_NAME: step_name,
          PropertyNames.OUTPUT_NAME: PropertyNames.OUT
      }
      si_labels[side_pval] = si_label

    # Now create the step for the ParDo transform being handled.
    transform_name = transform_node.full_label.rsplit('/', 1)[-1]
    step = self._add_step(
        TransformNames.DO,
        transform_node.full_label +
        ('/{}'.format(transform_name) if transform_node.side_inputs else ''),
        transform_node,
        transform_node.transform.output_tags)
    # Import here to avoid adding the dependency for local running scenarios.
    # pylint: disable=wrong-import-order, wrong-import-position
    from apache_beam.runners.dataflow.internal import apiclient
    transform_proto = self.proto_context.transforms.get_proto(transform_node)
    transform_id = self.proto_context.transforms.get_id(transform_node)
    use_fnapi = apiclient._use_fnapi(options)
    use_unified_worker = apiclient._use_unified_worker(options)
    # Patch side input ids to be unique across a given pipeline.
    if (label_renames and
        transform_proto.spec.urn == common_urns.primitives.PAR_DO.urn):
      # Patch PTransform proto.
      for old, new in label_renames.items():
        transform_proto.inputs[new] = transform_proto.inputs[old]
        del transform_proto.inputs[old]

      # Patch ParDo proto.
      proto_type, _ = beam.PTransform._known_urns[transform_proto.spec.urn]
      proto = proto_utils.parse_Bytes(transform_proto.spec.payload, proto_type)
      for old, new in label_renames.items():
        proto.side_inputs[new].CopyFrom(proto.side_inputs[old])
        del proto.side_inputs[old]
      transform_proto.spec.payload = proto.SerializeToString()
      # We need to update the pipeline proto.
      del self.proto_pipeline.components.transforms[transform_id]
      (
          self.proto_pipeline.components.transforms[transform_id].CopyFrom(
              transform_proto))
    # The data transmitted in SERIALIZED_FN is different depending on whether
    # this is a fnapi pipeline or not.
    if (use_fnapi and
        (transform_proto.spec.urn == common_urns.primitives.PAR_DO.urn or
         use_unified_worker)):
      serialized_data = transform_id
    else:
      serialized_data = pickler.dumps(
          self._pardo_fn_data(transform_node, lookup_label))
    step.add_property(PropertyNames.SERIALIZED_FN, serialized_data)
    # TODO(BEAM-8882): Enable once dataflow service doesn't reject this.
    # step.add_property(PropertyNames.PIPELINE_PROTO_TRANSFORM_ID, transform_id)
    step.add_property(
        PropertyNames.PARALLEL_INPUT,
        {
            '@type': 'OutputReference',
            PropertyNames.STEP_NAME: input_step.proto.name,
            PropertyNames.OUTPUT_NAME: input_step.get_output(input_tag)
        })
    # Add side inputs if any.
    step.add_property(PropertyNames.NON_PARALLEL_INPUTS, si_dict)

    # Generate description for the outputs. The output names
    # will be 'None' for main output and '<tag>' for a tagged output.
    outputs = []

    all_output_tags = list(transform_proto.outputs.keys())

    # Some external transforms require output tags to not be modified.
    # So we randomly select one of the output tags as the main output and
    # leave others as side outputs. Transform execution should not change
    # dependending on which output tag we choose as the main output here.
    # Also, some SDKs do not work correctly if output tags are modified. So for
    # external transforms, we leave tags unmodified.
    #
    # Python SDK uses 'None' as the tag of the main output.
    main_output_tag = 'None'

    step.encoding = self._get_encoded_output_coder(
        transform_node, output_tag=main_output_tag)

    side_output_tags = set(all_output_tags).difference({main_output_tag})

    # Add the main output to the description.
    outputs.append({
        PropertyNames.USER_NAME: (
            '%s.%s' % (transform_node.full_label, PropertyNames.OUT)),
        PropertyNames.ENCODING: step.encoding,
        PropertyNames.OUTPUT_NAME: main_output_tag
    })
    for side_tag in side_output_tags:
      # The assumption here is that all outputs will have the same typehint
      # and coder as the main output. This is certainly the case right now
      # but conceivably it could change in the future.
      encoding = self._get_encoded_output_coder(
          transform_node, output_tag=side_tag)
      outputs.append({
          PropertyNames.USER_NAME: (
              '%s.%s' % (transform_node.full_label, side_tag)),
          PropertyNames.ENCODING: encoding,
          PropertyNames.OUTPUT_NAME: side_tag
      })

    step.add_property(PropertyNames.OUTPUT_INFO, outputs)

    # Add the restriction encoding if we are a splittable DoFn
    # and are using the Fn API on the unified worker.
    restriction_coder = transform.get_restriction_coder()
    if restriction_coder:
      step.add_property(
          PropertyNames.RESTRICTION_ENCODING,
          self._get_cloud_encoding(restriction_coder))

    if options.view_as(StandardOptions).streaming:
      is_stateful_dofn = (DoFnSignature(transform.dofn).is_stateful_dofn())
      if is_stateful_dofn:
        step.add_property(PropertyNames.USES_KEYED_STATE, 'true')

        # Also checks whether the step allows shardable keyed states.
        # TODO(BEAM-11360): remove this when migrated to portable job
        #  submission since we only consider supporting the property in runner
        #  v2.
        for pcoll in transform_node.outputs.values():
          if pcoll._unique_name() in self.get_pcoll_with_auto_sharding():
            step.add_property(PropertyNames.ALLOWS_SHARDABLE_STATE, 'true')
            # Currently we only allow auto-sharding to be enabled through the
            # GroupIntoBatches transform. So we also add the following property
            # which GroupIntoBatchesDoFn has, to allow the backend to perform
            # graph optimization.
            step.add_property(PropertyNames.PRESERVES_KEYS, 'true')
            break

  @staticmethod
  def _pardo_fn_data(transform_node, get_label):
    transform = transform_node.transform
    si_tags_and_types = [  # pylint: disable=protected-access
        (get_label(side_pval), side_pval.__class__, side_pval._view_options())
        for side_pval in transform_node.side_inputs]
    return (
        transform.fn,
        transform.args,
        transform.kwargs,
        si_tags_and_types,
        transform_node.inputs[0].windowing)

  def run_CombineValuesReplacement(self, transform_node, options):
    transform = transform_node.transform.transform
    input_tag = transform_node.inputs[0].tag
    input_step = self._cache.get_pvalue(transform_node.inputs[0])
    step = self._add_step(
        TransformNames.COMBINE, transform_node.full_label, transform_node)
    transform_id = self.proto_context.transforms.get_id(transform_node.parent)

    # The data transmitted in SERIALIZED_FN is different depending on whether
    # this is a fnapi pipeline or not.
    from apache_beam.runners.dataflow.internal import apiclient
    use_fnapi = apiclient._use_fnapi(options)
    if use_fnapi:
      # Fnapi pipelines send the transform ID of the CombineValues transform's
      # parent composite because Dataflow expects the ID of a CombinePerKey
      # transform.
      serialized_data = transform_id
    else:
      # Combiner functions do not take deferred side-inputs (i.e. PValues) and
      # therefore the code to handle extra args/kwargs is simpler than for the
      # DoFn's of the ParDo transform. In the last, empty argument is where
      # side inputs information would go.
      serialized_data = pickler.dumps(
          (transform.fn, transform.args, transform.kwargs, ()))
    step.add_property(PropertyNames.SERIALIZED_FN, serialized_data)
    # TODO(BEAM-8882): Enable once dataflow service doesn't reject this.
    # step.add_property(PropertyNames.PIPELINE_PROTO_TRANSFORM_ID, transform_id)
    step.add_property(
        PropertyNames.PARALLEL_INPUT,
        {
            '@type': 'OutputReference',
            PropertyNames.STEP_NAME: input_step.proto.name,
            PropertyNames.OUTPUT_NAME: input_step.get_output(input_tag)
        })
    # Note that the accumulator must not have a WindowedValue encoding, while
    # the output of this step does in fact have a WindowedValue encoding.
    accumulator_encoding = self._get_cloud_encoding(
        transform.fn.get_accumulator_coder())
    output_encoding = self._get_encoded_output_coder(transform_node)

    step.encoding = output_encoding
    step.add_property(PropertyNames.ENCODING, accumulator_encoding)
    # Generate description for main output 'out.'
    outputs = []
    # Add the main output to the description.
    outputs.append({
        PropertyNames.USER_NAME: (
            '%s.%s' % (transform_node.full_label, PropertyNames.OUT)),
        PropertyNames.ENCODING: step.encoding,
        PropertyNames.OUTPUT_NAME: PropertyNames.OUT
    })
    step.add_property(PropertyNames.OUTPUT_INFO, outputs)

  def run_Read(self, transform_node, options):
    transform = transform_node.transform
    step = self._add_step(
        TransformNames.READ, transform_node.full_label, transform_node)
    # TODO(mairbek): refactor if-else tree to use registerable functions.
    # Initialize the source specific properties.

    standard_options = options.view_as(StandardOptions)
    if not hasattr(transform.source, 'format'):
      # If a format is not set, we assume the source to be a custom source.
      source_dict = {}

      source_dict['spec'] = {
          '@type': names.SOURCE_TYPE,
          names.SERIALIZED_SOURCE_KEY: pickler.dumps(transform.source)
      }

      try:
        source_dict['metadata'] = {
            'estimated_size_bytes': json_value.get_typed_value_descriptor(
                transform.source.estimate_size())
        }
      except error.RuntimeValueProviderError:
        # Size estimation is best effort, and this error is by value provider.
        _LOGGER.info(
            'Could not estimate size of source %r due to ' + \
            'RuntimeValueProviderError', transform.source)
      except Exception:  # pylint: disable=broad-except
        # Size estimation is best effort. So we log the error and continue.
        _LOGGER.info(
            'Could not estimate size of source %r due to an exception: %s',
            transform.source,
            traceback.format_exc())

      step.add_property(PropertyNames.SOURCE_STEP_INPUT, source_dict)
    elif transform.source.format == 'pubsub':
      if not standard_options.streaming:
        raise ValueError(
            'Cloud Pub/Sub is currently available for use '
            'only in streaming pipelines.')
      # Only one of topic or subscription should be set.
      if transform.source.full_subscription:
        step.add_property(
            PropertyNames.PUBSUB_SUBSCRIPTION,
            transform.source.full_subscription)
      elif transform.source.full_topic:
        step.add_property(
            PropertyNames.PUBSUB_TOPIC, transform.source.full_topic)
      if transform.source.id_label:
        step.add_property(
            PropertyNames.PUBSUB_ID_LABEL, transform.source.id_label)
      if transform.source.with_attributes:
        # Setting this property signals Dataflow runner to return full
        # PubsubMessages instead of just the data part of the payload.
        step.add_property(PropertyNames.PUBSUB_SERIALIZED_ATTRIBUTES_FN, '')

      if transform.source.timestamp_attribute is not None:
        step.add_property(
            PropertyNames.PUBSUB_TIMESTAMP_ATTRIBUTE,
            transform.source.timestamp_attribute)
    else:
      raise ValueError(
          'Source %r has unexpected format %s.' %
          (transform.source, transform.source.format))

    if not hasattr(transform.source, 'format'):
      step.add_property(PropertyNames.FORMAT, names.SOURCE_FORMAT)
    else:
      step.add_property(PropertyNames.FORMAT, transform.source.format)

    # Wrap coder in WindowedValueCoder: this is necessary as the encoding of a
    # step should be the type of value outputted by each step.  Read steps
    # automatically wrap output values in a WindowedValue wrapper, if necessary.
    # This is also necessary for proper encoding for size estimation.
    # Using a GlobalWindowCoder as a place holder instead of the default
    # PickleCoder because GlobalWindowCoder is known coder.
    # TODO(robertwb): Query the collection for the windowfn to extract the
    # correct coder.
    coder = coders.WindowedValueCoder(
        coders.registry.get_coder(transform_node.outputs[None].element_type),
        coders.coders.GlobalWindowCoder())

    step.encoding = self._get_cloud_encoding(coder)
    step.add_property(
        PropertyNames.OUTPUT_INFO,
        [{
            PropertyNames.USER_NAME: (
                '%s.%s' % (transform_node.full_label, PropertyNames.OUT)),
            PropertyNames.ENCODING: step.encoding,
            PropertyNames.OUTPUT_NAME: PropertyNames.OUT
        }])

  def run__NativeWrite(self, transform_node, options):
    transform = transform_node.transform
    input_tag = transform_node.inputs[0].tag
    input_step = self._cache.get_pvalue(transform_node.inputs[0])
    step = self._add_step(
        TransformNames.WRITE, transform_node.full_label, transform_node)
    # TODO(mairbek): refactor if-else tree to use registerable functions.
    # Initialize the sink specific properties.
    if transform.sink.format == 'pubsub':
      standard_options = options.view_as(StandardOptions)
      if not standard_options.streaming:
        raise ValueError(
            'Cloud Pub/Sub is currently available for use '
            'only in streaming pipelines.')
      step.add_property(PropertyNames.PUBSUB_TOPIC, transform.sink.full_topic)
      if transform.sink.id_label:
        step.add_property(
            PropertyNames.PUBSUB_ID_LABEL, transform.sink.id_label)
      # Setting this property signals Dataflow runner that the PCollection
      # contains PubsubMessage objects instead of just raw data.
      step.add_property(PropertyNames.PUBSUB_SERIALIZED_ATTRIBUTES_FN, '')
      if transform.sink.timestamp_attribute is not None:
        step.add_property(
            PropertyNames.PUBSUB_TIMESTAMP_ATTRIBUTE,
            transform.sink.timestamp_attribute)
    else:
      raise ValueError(
          'Sink %r has unexpected format %s.' %
          (transform.sink, transform.sink.format))
    step.add_property(PropertyNames.FORMAT, transform.sink.format)

    # Wrap coder in WindowedValueCoder: this is necessary for proper encoding
    # for size estimation. Using a GlobalWindowCoder as a place holder instead
    # of the default PickleCoder because GlobalWindowCoder is known coder.
    # TODO(robertwb): Query the collection for the windowfn to extract the
    # correct coder.
    coder = coders.WindowedValueCoder(
        transform.sink.coder, coders.coders.GlobalWindowCoder())
    step.encoding = self._get_cloud_encoding(coder)
    step.add_property(PropertyNames.ENCODING, step.encoding)
    step.add_property(
        PropertyNames.PARALLEL_INPUT,
        {
            '@type': 'OutputReference',
            PropertyNames.STEP_NAME: input_step.proto.name,
            PropertyNames.OUTPUT_NAME: input_step.get_output(input_tag)
        })

  def run_TestStream(self, transform_node, options):
    from apache_beam.testing.test_stream import ElementEvent
    from apache_beam.testing.test_stream import ProcessingTimeEvent
    from apache_beam.testing.test_stream import WatermarkEvent
    standard_options = options.view_as(StandardOptions)
    if not standard_options.streaming:
      raise ValueError(
          'TestStream is currently available for use '
          'only in streaming pipelines.')

    transform = transform_node.transform
    step = self._add_step(
        TransformNames.READ, transform_node.full_label, transform_node)
    step.add_property(
        PropertyNames.SERIALIZED_FN,
        self.proto_context.transforms.get_id(transform_node))
    step.add_property(PropertyNames.FORMAT, 'test_stream')
    test_stream_payload = beam_runner_api_pb2.TestStreamPayload()
    # TestStream source doesn't do any decoding of elements,
    # so we won't set test_stream_payload.coder_id.
    output_coder = transform._infer_output_coder()  # pylint: disable=protected-access
    for event in transform._events:
      new_event = test_stream_payload.events.add()
      if isinstance(event, ElementEvent):
        for tv in event.timestamped_values:
          element = new_event.element_event.elements.add()
          element.encoded_element = output_coder.encode(tv.value)
          element.timestamp = tv.timestamp.micros
      elif isinstance(event, ProcessingTimeEvent):
        new_event.processing_time_event.advance_duration = (
            event.advance_by.micros)
      elif isinstance(event, WatermarkEvent):
        new_event.watermark_event.new_watermark = event.new_watermark.micros
    serialized_payload = self.byte_array_to_json_string(
        test_stream_payload.SerializeToString())
    step.add_property(PropertyNames.SERIALIZED_TEST_STREAM, serialized_payload)

    step.encoding = self._get_encoded_output_coder(transform_node)
    step.add_property(
        PropertyNames.OUTPUT_INFO,
        [{
            PropertyNames.USER_NAME: (
                '%s.%s' % (transform_node.full_label, PropertyNames.OUT)),
            PropertyNames.ENCODING: step.encoding,
            PropertyNames.OUTPUT_NAME: PropertyNames.OUT
        }])

  # We must mark this method as not a test or else its name is a matcher for
  # nosetest tests.
  run_TestStream.__test__ = False  # type: ignore[attr-defined]

  @classmethod
  def serialize_windowing_strategy(cls, windowing, default_environment):
    from apache_beam.runners import pipeline_context
    context = pipeline_context.PipelineContext(
        default_environment=default_environment)
    windowing_proto = windowing.to_runner_api(context)
    return cls.byte_array_to_json_string(
        beam_runner_api_pb2.MessageWithComponents(
            components=context.to_runner_api(),
            windowing_strategy=windowing_proto).SerializeToString())

  @classmethod
  def deserialize_windowing_strategy(cls, serialized_data):
    # Imported here to avoid circular dependencies.
    # pylint: disable=wrong-import-order, wrong-import-position
    from apache_beam.runners import pipeline_context
    from apache_beam.transforms.core import Windowing
    proto = beam_runner_api_pb2.MessageWithComponents()
    proto.ParseFromString(cls.json_string_to_byte_array(serialized_data))
    return Windowing.from_runner_api(
        proto.windowing_strategy,
        pipeline_context.PipelineContext(proto.components))

  @staticmethod
  def byte_array_to_json_string(raw_bytes):
    """Implements org.apache.beam.sdk.util.StringUtils.byteArrayToJsonString."""
    return quote(raw_bytes)

  @staticmethod
  def json_string_to_byte_array(encoded_string):
    """Implements org.apache.beam.sdk.util.StringUtils.jsonStringToByteArray."""
    return unquote_to_bytes(encoded_string)

  def get_default_gcp_region(self):
    """Get a default value for Google Cloud region according to
    https://cloud.google.com/compute/docs/gcloud-compute/#default-properties.
    If no default can be found, returns None.
    """
    environment_region = os.environ.get('CLOUDSDK_COMPUTE_REGION')
    if environment_region:
      _LOGGER.info(
          'Using default GCP region %s from $CLOUDSDK_COMPUTE_REGION',
          environment_region)
      return environment_region
    try:
      cmd = ['gcloud', 'config', 'get-value', 'compute/region']
      raw_output = processes.check_output(cmd, stderr=DEVNULL)
      formatted_output = raw_output.decode('utf-8').strip()
      if formatted_output:
        _LOGGER.info(
            'Using default GCP region %s from `%s`',
            formatted_output,
            ' '.join(cmd))
        return formatted_output
    except RuntimeError:
      pass
    return None

class _DataflowSideInput(beam.pvalue.AsSideInput):
  """Wraps a side input as a dataflow-compatible side input."""
  def _view_options(self):
    return {
        'data': self._data,
    }

  def _side_input_data(self):
    return self._data

class _DataflowIterableAsMultimapSideInput(_DataflowSideInput):
  """Wraps an iterable side input as dataflow-compatible side input."""
  def __init__(self, side_input):
    # pylint: disable=protected-access
    side_input_data = side_input._side_input_data()
    assert (
        side_input_data.access_pattern == common_urns.side_inputs.ITERABLE.urn)
    iterable_view_fn = side_input_data.view_fn
    self._data = beam.pvalue.SideInputData(
        common_urns.side_inputs.MULTIMAP.urn,
        side_input_data.window_mapping_fn,
        lambda multimap: iterable_view_fn(multimap[b'']))

class _DataflowIterableSideInput(_DataflowSideInput):
  """Wraps an iterable side input as dataflow-compatible side input."""
  def __init__(self, side_input):
    # pylint: disable=protected-access
    self.pvalue = side_input.pvalue
    side_input_data = side_input._side_input_data()
    assert (
        side_input_data.access_pattern == common_urns.side_inputs.ITERABLE.urn)
    self._data = beam.pvalue.SideInputData(
        common_urns.side_inputs.ITERABLE.urn,
        side_input_data.window_mapping_fn,
        side_input_data.view_fn)

class _DataflowMultimapSideInput(_DataflowSideInput):
  """Wraps a multimap side input as dataflow-compatible side input."""
  def __init__(self, side_input):
    # pylint: disable=protected-access
    self.pvalue = side_input.pvalue
    side_input_data = side_input._side_input_data()
    assert (
        side_input_data.access_pattern == common_urns.side_inputs.MULTIMAP.urn)
    self._data = beam.pvalue.SideInputData(
        common_urns.side_inputs.MULTIMAP.urn,
        side_input_data.window_mapping_fn,
        side_input_data.view_fn)

class DataflowPipelineResult(PipelineResult):
  """Represents the state of a pipeline run on the Dataflow service."""
  def __init__(self, job, runner):
    """Initialize a new DataflowPipelineResult instance.

    Args:
      job: Job message from the Dataflow API. Could be :data:`None` if a job
        request was not sent to Dataflow service (e.g. template jobs).
      runner: DataflowRunner instance.
    """
    self._job = job
    self._runner = runner
    self.metric_results = None

  def _update_job(self):
    # We need the job id to be able to update job information. There is no need
    # to update the job if we are in a known terminal state.
    if self.has_job and not self.is_in_terminal_state():
      self._job = self._runner.dataflow_client.get_job(self.job_id())

  def job_id(self):
    return self._job.id

  def metrics(self):
    return self.metric_results

  @property
  def has_job(self):
    return self._job is not None

  @staticmethod
  def api_jobstate_to_pipeline_state(api_jobstate):
    values_enum = dataflow_api.Job.CurrentStateValueValuesEnum

    # Ordered by the enum values. Values that may be introduced in
    # future versions of Dataflow API are considered UNRECOGNIZED by this SDK.
    api_jobstate_map = defaultdict(
        lambda: PipelineState.UNRECOGNIZED,
        {
            values_enum.JOB_STATE_UNKNOWN: PipelineState.UNKNOWN,
            values_enum.JOB_STATE_STOPPED: PipelineState.STOPPED,
            values_enum.JOB_STATE_RUNNING: PipelineState.RUNNING,
            values_enum.JOB_STATE_DONE: PipelineState.DONE,
            values_enum.JOB_STATE_FAILED: PipelineState.FAILED,
            values_enum.JOB_STATE_CANCELLED: PipelineState.CANCELLED,
            values_enum.JOB_STATE_UPDATED: PipelineState.UPDATED,
            values_enum.JOB_STATE_DRAINING: PipelineState.DRAINING,
            values_enum.JOB_STATE_DRAINED: PipelineState.DRAINED,
            values_enum.JOB_STATE_PENDING: PipelineState.PENDING,
            values_enum.JOB_STATE_CANCELLING: PipelineState.CANCELLING,
            values_enum.JOB_STATE_RESOURCE_CLEANING_UP: PipelineState.
            RESOURCE_CLEANING_UP,
        })

    return (
        api_jobstate_map[api_jobstate]
        if api_jobstate else PipelineState.UNKNOWN)

  def _get_job_state(self):
    return self.api_jobstate_to_pipeline_state(self._job.currentState)

  @property
  def state(self):
    """Return the current state of the remote job.

    Returns:
      A PipelineState object.
    """
    if not self.has_job:
      return PipelineState.UNKNOWN

    self._update_job()

    return self._get_job_state()

  def is_in_terminal_state(self):
    if not self.has_job:
      return True

    return PipelineState.is_terminal(self._get_job_state())

  def wait_until_finish(self, duration=None):
    if not self.is_in_terminal_state():
      if not self.has_job:
        raise IOError('Failed to get the Dataflow job id.')
      consoleUrl = (
          "Console URL: https://console.cloud.google.com/"
          f"dataflow/jobs/<RegionId>/{self.job_id()}"
          "?project=<ProjectId>")
      thread = threading.Thread(
          target=DataflowRunner.poll_for_job_completion,
          args=(self._runner, self, duration))

      # Mark the thread as a daemon thread so a keyboard interrupt on the main
      # thread will terminate everything. This is also the reason we will not
      # use thread.join() to wait for the polling thread.
      thread.daemon = True
      thread.start()
      while thread.is_alive():
        time.sleep(5.0)

      # TODO: Merge the termination code in poll_for_job_completion and
      # is_in_terminal_state.
      terminated = self.is_in_terminal_state()
      assert duration or terminated, (
          'Job did not reach to a terminal state after waiting indefinitely. '
          '{}'.format(consoleUrl))

      # TODO(https://github.com/apache/beam/issues/21695): Also run this check
      # if wait_until_finish was called after the pipeline completed.
      if terminated and self.state != PipelineState.DONE:
        # TODO(BEAM-1290): Consider converting this to an error log based on
        # theresolution of the issue.
        _LOGGER.error(consoleUrl)
        raise DataflowRuntimeException(
            'Dataflow pipeline failed. State: %s, Error:\n%s' %
            (self.state, getattr(self._runner, 'last_error_msg', None)),
            self)

    return self.state

  def cancel(self):
    if not self.has_job:
      raise IOError('Failed to get the Dataflow job id.')

    self._update_job()

    if self.is_in_terminal_state():
      _LOGGER.warning(
          'Cancel failed because job %s is already terminated in state %s.',
          self.job_id(),
          self.state)
    else:
      if not self._runner.dataflow_client.modify_job_state(
          self.job_id(), 'JOB_STATE_CANCELLED'):
        cancel_failed_message = (
            'Failed to cancel job %s, please go to the Developers Console to '
            'cancel it manually.') % self.job_id()
        _LOGGER.error(cancel_failed_message)
        raise DataflowRuntimeException(cancel_failed_message, self)

    return self.state

  def __str__(self):
    return '<%s %s %s>' % (self.__class__.__name__, self.job_id(), self.state)

  def __repr__(self):
    return '<%s %s at %s>' % (self.__class__.__name__, self._job, hex(id(self)))

class DataflowRuntimeException(Exception):
  """Indicates an error has occurred in running this pipeline."""
  def __init__(self, msg, result):
    super().__init__(msg)
    self.result = result


text:
Q: How to zoom web view programmatically? Is any idea, how to set zoom level of webview by programming ?
Thanks

A: Probably this thread solves your problem? iPhone UIWebView - How do you set the zoom level and position?
alex

redpajama_set_name: RedPajamaStackExchange


text:
Q: tinymce3 editor in joomla 1.5 browse button not working i have a strange issue i have a joomla 1.5 site and i have installed the tinymce3 cause the client needs really bad this button that it has ,,that is the paste from word,, the problem is that when i insert for example a link or an image then when i click the browse button to browse files and content then nothing happens with the onclick,, :(
i have searched through the net and found MCImageManager might be a solution but i have to buy it.. is there any other way ?
any help would be really appreciated,
thank you guys..

A: Hi guys in case someone else need it,
just instead of tinymce editor i used the jce editor,
solved just from the jce editor it provides this kind of functionality the paste from word
just by playing on the default profile plugging settings parameters
and the browse button is working like a charm,
once again thank you


text:
State/OIG Report on ECA Program – Whew! What a relief that this is so boring to everyone!
August 4, 2015 By domani spero in Govt Documents, Hillary, Political Appointees, Secretary of State, Staffing the FS, State Department Tags: boring, Bureau of Educational and Cultural Affairs, Cheryl Mills, Exchange Program, Maura Pally, OIG Report, State/ECA, State/OIG
On October 24, 2009, the AP reported the following:
The State Department's internal watchdog concludes in a new report that the nation's student foreign exchange programs need better federal oversight, after a scandal in which students were placed in shoddy homes in Pennsylvania.
The State Department's acting inspector general, Harold W. Geisel, said in the report that the department's Bureau of Educational and Cultural Affairs had largely abdicated its responsibility to ensure the well-being of thousands of exchange students who come to the United States each year.
The day before, the acting State/ECA boss wrote an email to Cheryl Mills on the IG report press coverage:
Via foia.state.gov
Email in pdf here.
Maura Pally is the the Senior Vice President, Women and Youth Programs, and serves as Acting CEO a the Clinton Foundation. Previously, she served as Acting Assistant and Deputy Assistant Secretary of State for Educational and Cultural Affairs (ECA). Prior to joining the U.S. Department of State, she also served as deputy counsel for Secretary Clinton's first presidential campaign.
Reuters: State Department watered down human trafficking report
August 4, 2015 By domani spero in Congress, Controversies, Govt Documents, Political Appointees, State Department, Trafficking, U.S. Missions Tags: Cuba, India, Malaysia, Mexico, Sarah Sewall, State/J, State/J/TIP, Trafficking in Persons Report 2015, Under Secretary for Civilian Security Democracy and Human Rights, Uzbekistan	1 Comment
United States upgrades Malaysia and Cuba in annual report on human trafficking: http://t.co/MgWPwlHDvE pic.twitter.com/LEJHq0a5LO
— Reuters Top News (@Reuters) July 27, 2015
Special Report: State Department watered down human trafficking report http://t.co/CEzvApslKD
— Taryn Wilson (@Teedubbleyou) August 4, 2015
In the weeks leading up to a critical annual U.S. report on human trafficking that publicly shames the world's worst offenders, human rights experts at the State Department concluded that trafficking conditions hadn't improved in Malaysia and Cuba. And in China, they found, things had grown worse.
The State Department's senior political staff saw it differently — and they prevailed.
A Reuters examination, based on interviews with more than a dozen sources in Washington and foreign capitals, shows that the government office set up to independently grade global efforts to fight human trafficking was repeatedly overruled by senior American diplomats and pressured into inflating assessments of 14 strategically important countries in this year's Trafficking in Persons report.
In all, analysts in the Office to Monitor and Combat Trafficking in Persons – or J/TIP, as it's known within the U.S. government — disagreed with U.S. diplomatic bureaus on ratings for 17 countries, the sources said.
The analysts, who are specialists in assessing efforts to combat modern slavery – such as the illegal trade in humans for forced labor or prostitution – won only three of those disputes, the worst ratio in the 15-year history of the unit, according to the sources.
As a result, not only Malaysia, Cuba and China, but countries such as India, Uzbekistan and Mexico, wound up with better grades than the State Department's human-rights experts wanted to give them, the sources said. (Graphic looking at some of the key decisions here: reut.rs/1gF2Wz5)
The final decision on disputed rankings this year was made in meetings attended by some of the State Department's most powerful diplomats, including Deputy Secretary of State Tony Blinken, Under Secretary of State for Political Affairs Wendy Sherman and Kerry's Chief of Staff, Jonathan Finer, according to the sources.
Read in full here.
Related item:
-07/27/15 Under Secretary of State for Civilian Security, Democracy, and Human Rights Sarah Sewall on the 2015 Trafficking in Persons Report; Under Secretary for Civilian Security, Democracy, and Human Rights Sarah Sewall; Washington, DC
State Dept Honors Six Security Contractors Killed in 2014 Camp Gibson-Kabul Suicide Attack
August 4, 2015 By domani spero in Contractors, Memorial, Security, State Department, US Embassy Kabul, War Tags: Afghanistan, Camp Gibson, contractors-killed, DynCorp International, Heather Higginbottom, Kabul, Private Security Contractors, State/INL, US Mission Afghanistan, William R. Brownfield
On August 3, the State Department held a ceremony honoring six security personnel who were killed while working for DynCorp International on behalf of the Bureau of International Narcotics and Law Enforcement Affairs (INL) in Afghanistan.
All six honorees were security guards at Camp Gibson in Kabul and were killed on July 22, 2014, when a suicide bomber riding a motorcycle attacked the camp. They hailed from four different countries – Fiji, India, Kenya, and Nepal. Deputy Secretary Heather Higginbottom and INL Assistant Secretary William R. Brownfield will pay tribute to our fallen colleagues by laying a wreath at the INL Memorial Wall located within the State Department building at its 21st Street Entrance.
There are 93 names on the wall commemorating the individuals from 12 countries and the United States who lost their lives between 1989 and 2014 while supporting the Department's criminal justice assistance programs abroad. These individuals collaborated with host governments and civil society in challenging environments to enhance respect for rule of law around the world. The Department is proud to recognize their service and sacrifice to our nation.
A virtual INL Memorial Wall is available at http://www.state.gov/j/inl/inlvirtualwall to pay tribute to the 93 honorees and their families.
Today A/S Brownfield & @hhigginbottom paid tribute to colleagues who fell in the line of duty http://t.co/ilU0OF0Bycpic.twitter.com/En0Aas8ZHz
— US Dept of State INL (@StateINL) August 3, 2015
The State Department announcement does not include the names of those honored at the INL ceremony. The New Indian Express identified the two Indian nationals as P V Kuttappan and Raveendran Parambath, as well as the two Nepali security guards as Ganga Limbu and Anil Gurung. The security guards from Fiji and Kenya were not identified.

redpajama_set_name: RedPajamaCommonCrawl


text:
Rob Lavery: Solo Journey
This is a fascinating and oft times invigorating album: it's literally 'All Over The Place' – and I mean that in a very positive sense – but it also, because of the sequencing of the songs, comes across as a really enjoyable cohesive whole. Equally eye opening (well, Ear opening actually) is the diversity of musical influences that permeate these 9 delightfully different tunes: I hear everything/everyone from rambunctious jazz fusion monsters like John McLaughlin & Larry Coryell, sinewy guitar lines like those of Pat Martino right through exuberant and wild Frank Zappa sounding material. (Yeah: Good Fun!)
Another facet I found interesting is the writer/player's compositional approach and the arrangements (these are all originals). At one point I was thinking of noting that the structure of the album reminded me a little of how McLaughlin interspaces deep, contemplative laid back songs between his barn burners— but in all candor (and I guess I'm allowed to say this since Mr. Lavery plays nearly all the instruments on the CD) I can't help but feel that this artist wrote his playbook concerning 'how to play the Studio' itself based on the legacy of a certain Mr. Joe Zawinul.
Now, while I'd recommend playing this from beginning to end since – as I'd noted – the entire series of songs congeal beautifully into a single whole, it's also worth mentioning that this album delivers a diverse variety of complimentary grooves and fires to suit any mood: if you're looking for something orchestral and (yeah, I have to say it) genuinely pretty, The Highlander, Everything We Like and In Accordance to Your Dreams will satisfy your needs: plus these lyrical tunes provide some of the most rocking guitar lines on the album. If, on the other hand, you're hungry for upbeat, burning songs to send you into orbit there are the Zappa-esqe Gland War, The Waylock and The Wedge Factor.
— Then there are the songs I just don't know how to label. Take Atonal Wanderer for instance: it begins in such a blissful, laid back groove, only to become a launching pad for some of the most scintillating guitar playing on the CD. Killer! And I Am You Are, which starts off in a 5/8 sophisticated latin Fusion groove before before bopping' into some insanely lyrical straight ahead jazz guitar which *then* morphs into the kind of blazing rockish burning (what a tone!) most rock guitarists simply dream of being able to pull off.
(Frankly, I don't know how to categorize this playing; is it rock/jazz/fusion? I'd say all of the above. Suffice it to say that – considering this gent's blistering technique – it's evident those fingers of his can execute any thought that comes into this guitarist's head, and his playing proves that head is full of enervating and stimulating ideas. This album makes it quite apparent that here's a man who's been listening to *all* kinds of music all his life.)
I'm shying away from a track by track analysis here: I can't help but feel I'd be doing this fabulous music an injustice.
— Need I say that I strongly recommend you folks find out for yourselves (and that I hope the next album is a LIVE one)?!
Rob Lavery, Solo Journey


What is the 'point of sale'?
In the world of retail, the 'point of sale' is defined as the time or place at which a customer completes a purchase – and for retailers looking to level up their revenue, point of sale displays are the key. By harnessing best practices from the POS rulebook, store owners can elevate their business to a new level of profitability.
Professional point of sale materials give retailers the tools they need to tempt customers into making more purchases per visit – and can even convert interested window shoppers into new paying customers. At shop4pop.com, we specialise in superior quality point of purchase (POP) displays – with a range of unique products tailor-made to enhance your in-store experience.
By utilising a number of retail display stands and signs throughout your shop, you can harness the power of professional branding and maximise the success of every special offer, seasonal promotion and high-margin product.
FSDUs: these free-standing display units are an ideal way to advertise impulse purchases, with a huge number of customisation options to help you make your POP display your own.
Dump bins: every state-of-the-art dump bin offers a double whammy of space-saving storage and professionally branded promotional space – with personalisation options galore.
Hanging signs: our high quality hanging banners and signs are a great way to get eyes on your latest offers – giving in-store promotions pride of place at an affordable price.
Display cubes: these flexible cubes are perfect as either standalone or stackable point of sale materials, bringing maximum attention to seasonal promotions and special offers.
Counter top units: whether you're looking for counter top literature stands or bespoke leaflet holders, these compact and portable display units are sure to do the trick.
Totems and bollards: ideal for indoor or outdoor use, our high quality totems and bollard covers help retailers increase foot traffic and drive more customers to the point of sale.
Flyers: our professional printing solutions are perfect for store owners looking to print flyers online. Couple with shop4pop.com's counter top units for the ultimate POS display.
Posters: available in a range of sizes and fully customisable, these cost-effective promotional posters give you the tools to advertise time-sensitive offers in the highest possible print quality.
Tent cards: particularly popular in bars and restaurants, our compact tent cards can be used across multiple service points to make your point of sale display more profitable than ever.
Show cards: our high definition show cards, whether large or small, help your point of purchase to make an impact with customers – with dynamic design options guaranteed to suit your store.
Shelf wobblers: printed on durable PVC and easy to attach, shop4pop.com's nifty shelf wobblers are an eye-catching way to promote in-store deals and limited edition products.
Window clings: created using a superior self-cling film, these striking window displays come customised with your store in mind and are completely hassle-free.
shop4pop.com's point of sale displays give you a practical and powerful way to advertise key products and special offers – but it's your bespoke branding that takes these in-store materials to the next level.
Whether you want to customise one of our ready-made templates or upload your own unique design, every POS display can be personalised to incorporate your brand identity – and it couldn't be easier to create a product that suits your store.


This command adds the specified amount of government ability points to your current country.
Amount Optional - the amount of government ability points to add. If not specified, you will be granted the maximum amount of government ability points.
The above example would grant your currently the maximum amount of government ability points.
This command would add two government ability points to your country.


Copper Cow Coffee - Thai Iced Tea with Creamer, 5.6 oz.
This Thai Iced Tea with Creamer from Copper Cow Coffee is a real show stopper! With absolutely no preservatives, additives or artificial flavors, this Thai Iced Tea is delicious and easy to make. Simply combine tea sprinkles with 2 ounces of hot water and mix. Add 4 ounces of cold water and pour the tea over ice. Add in one packet of sweetened condensed milk and you are set! Enjoying Thai Iced Tea has never been tastier or simpler!
Facts: 5.6 oz. 160g. Product of USA.
Ingredients: Milk, sugar, Thai tea. Allergen Information: contains milk.


Grateful Dead Steal Your Face Patch (Silver) - ON SALE NOW!
Wear this super cool silver Grateful Dead "Steal Your Face" patch with pride on your sleeve... or on your hat, backpack, or anywhere else you want to express yourself! The shiny silver design makes this iron-on Steal Your Face patch bright, bold, and beautiful.


text:
Reason for reading – first person narrative
The story begins with the reaping and Katniss volunteering to take her sister's place. Written in the first person singular we know her thoughts, feelings and motivations. "How could I leave Priim, who is the only person in the world I'm certain I love?"
Like many dystopian novels this world is ruled by an oppressive regime which seeks to remind their citizens of the price of rebellion. Every year they take a boy and a girl from each district to fight in the gladiatorial arena. The leader is this regime is President Snow, an ironic name given that he is anything but pure in his motives. His symbol appears to be genetically modified white rose, heavy in its perfume it disguises the smell of blood, caused by the poison he made his enemies drink over the years. Almost like a sinister and benevolent Stalin, who rids himself of the competition, not quite the night of the long knives, but just as deadly.
The subsequent books in the series give more detail about the rebellion. There is the presumed destruction and abandonment of district 13, now the rebel base. The Quarter Quell where victors of the games compete in a special anniversary tournament. At first I felt this was merely a repetition of the themes of the first book, but it soon becomes clear these games are less of a competition and more about working together in support of the rebellion.
When the force field surrounding the arena is destroyed Katniss and her allies are rescued, but Peter is captured and tortured by the Capitol. Used in their propaganda war, his mind is manipulated so much that he no longer knows what is real. When he is rescued he becomes a liability, even trying to kill Katniss. Eventually the balance of his mind does return, but you do feel he'll be forever scarred.
I find the ending a little awkward. I understand Katniss's reasoning that the rebel leader may have been complicit in her sister's death, but I would have liked some more certainty. ,The idea of her being with Peeta and not Gale also seems wrong. In the beginning of the first book she declares, "There's never been anything romantic between Gale and me." She admits to being jealous when other girls find him attractive, "but not for the reason people would think. Good hunting partners are hard to find." You almost feel she is in denial and that she does like him more than she can admit to herself. When Gale is flogged she is distraught and they have to cover this up by saying he is her cousin. When Peeta is hit by the force field and Finnick saves his life she is equally distraught and grateful.
The limitation of first person narrative is that we have to see other characters through the eyes of the one person telling the story. This means that we have to interpret them through their eyes. Haymitch is shown as nothing but a drunk initially, but as the story progresses we learn far more about him and how much he cares. All his gifts in the arena, in the first book are for Katniss, even when he sends medicine for Peeta, it is Katniss he sends it to. The citizens of the Capitol come across as dizzy and shallow, but not cruel. Theirs is a fanciful, blind indoctrination based on ignorance. Plutarch being a notable exception, he carries the mockingjay symbol on his watch, as a sign of his support for the rebellion.


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Jammu and Kashmir: Hurriyat leaders ready for talks with Centre, says Governor Satya Pal Malik
Separatist leader Mirwaiz Umar Farooq emphasised that Centre should take an initiative to start the stalled dialogue.
Jun 23, 2019 · 01:51 pm
Jammu and Kashmir Governor Satya Pal Malik | IANS
Jammu and Kashmir Governor Satya Pal Malik on Saturday said that the situation in Jammu and Kashmir is changing and the leaders of the Hurriyat Conference, a group of separatist parties, were ready for talks with the Centre, ANI reported.
"The ones who turned back Ram Vilas Paswan from their door, they are now ready for talks," Malik was quoted as saying by the news agency. He was referring to Paswan's outreach to Hurriyat leaders during an unrest in 2016. Hurriyat hard-liner Syed Ali Shah Geelani had not opened the door of his house to welcome Paswan when he had approached him.
"The stone pelting after namaz is almost over, we do not like it when a youngster dies," Malik added. "But when a bullet is fired, bullets will be fired in retaliation. That is the reality and this change in stance by them [Hurriyat leaders] is a welcome sign." The governor was speaking at a Doordarshan event in Srinagar. He made these statements in the presence of Union Minister for Information and Broadcasting Prakash Javadekar and Minister of State in the Prime Minister's Office Jitendra Singh. The governor also expressed satisfaction over security management in the past year.
With Governor Shri Satya Pal Malik and I&B Minister Shri @PrakashJavdekar , distribution of Doordarshan free Dish Set Top boxes. Preference for people living along International Border & other border areas. pic.twitter.com/MDWynMUwT2
— Dr Jitendra Singh (@DrJitendraSingh) June 22, 2019
The Jammu and Kashmir Ittehadul Muslimeen, a party which belongs to the Hurriyat faction headed by separatist leader Mirwaiz Umar Farooq, on Friday expressed its willingness to hold talks with Centre.
In an interview to the Daily Excelsior, Farooq had emphasised that the Centre should take initiative to start the stalled dialogue process in the state and that such sincere efforts would get the desired response from them. "We will never shirk from our responsibility to take the political process forward and if Centre [shows] sincerity, [we] will respond in appropriate manner," Farooq said.
"New Delhi needs to initiate dialogue with the alienated section of Kashmiri society and simultaneously open channels with Pakistan," Farooq added. "Similar exercise was done during the Vajpayee era, when despite several obstacles, the process was taken forward at different levels".
"The new [Pakistan] government led by Imran Khan has on more than one occasion extended the offer for talks and therefore, we must give them a chance keeping aside the bitter experiences of the past," Farooq said, adding that Hurriyat was ready to act as a bridge between the two countries. "I know even Prime Minister Narendra Modi during his previous tenure also tried to improve ties with Pakistan, but things didn't move in the right direction".
In an interview to Scroll.in earlier this year, Farooq had claimed the leadership's agenda is only the resolution of the Kashmir dispute that is based on self-determination. He had also said that the only way to move forward was for India, Pakistan and the Kashmiris to engage with each other and there was no reason for the Indian government to create a problem about this.
The last time the Hurriyat leaders had held talks with the Centre was in 2004, when the National Democratic Alliance was in power. In 2005, they had met Prime Minister Manmohan Singh but there were no formal discussions. The talks have not progressed since then.
Mirwaiz Umar Farooq
Satya Pal Malik
Ram Vilas Paswan
Hurriyat Conference
J&K: Complete shutdown in Poonch to protest against Rajouri killings


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Il singolare del torneo di tennis Bratislava Challenger 1996, facente parte dell'ATP Challenger Series, ha avuto come vincitore David Škoch che ha battuto in finale Gustavo Kuerten 6-2, 6-4.

Teste di serie

 Jérôme Golmard (primo turno)
 Ján Krošlák (secondo turno)
 Lionel Roux (quarti di finale)
 Filippo Messori (primo turno)

 Gustavo Kuerten (finale)
 Thierry Guardiola (secondo turno)
 Patrik Fredriksson (quarti di finale)
 Tomas Nydahl (primo turno)

Tabellone

Finale

Parte alta

Parte bassa

Collegamenti esterni

Bratislava Challenger 1996

redpajama_set_name: RedPajamaWikipedia


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