221 lines
8.9 KiB
Python
221 lines
8.9 KiB
Python
# -*- coding: utf-8 -*-
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# Copyright (C) 2013 Yahoo! Inc. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License"); you may
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# not use this file except in compliance with the License. You may obtain
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# a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
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# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
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# License for the specific language governing permissions and limitations
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# under the License.
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import logging
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import threading
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import networkx as nx
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from taskflow import exceptions
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from taskflow import flow
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from taskflow.patterns import graph_flow as gf
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from taskflow.patterns import linear_flow as lf
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from taskflow.patterns import unordered_flow as uf
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from taskflow import retry
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from taskflow import task
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from taskflow.utils import graph_utils as gu
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from taskflow.utils import lock_utils as lu
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from taskflow.utils import misc
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LOG = logging.getLogger(__name__)
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# Use the 'flatten' attribute as the need to add an edge here, which is useful
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# for doing later analysis of the edges (to determine why the edges were
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# created).
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FLATTEN_EDGE_DATA = {
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'flatten': True,
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}
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class Flattener(object):
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def __init__(self, root, freeze=True):
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self._root = root
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self._graph = None
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self._history = set()
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self._freeze = bool(freeze)
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self._lock = threading.Lock()
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self._edge_data = FLATTEN_EDGE_DATA.copy()
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def _add_new_edges(self, graph, nodes_from, nodes_to, edge_attrs=None):
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"""Adds new edges from nodes to other nodes in the specified graph,
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with the following edge attributes (defaulting to the class provided
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edge_data if None), if the edge does not already exist.
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"""
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if edge_attrs is None:
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edge_attrs = self._edge_data
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else:
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edge_attrs = edge_attrs.copy()
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edge_attrs.update(self._edge_data)
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for u in nodes_from:
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for v in nodes_to:
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if not graph.has_edge(u, v):
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# NOTE(harlowja): give each edge its own attr copy so that
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# if it's later modified that the same copy isn't modified.
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graph.add_edge(u, v, attr_dict=edge_attrs.copy())
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def _flatten(self, item):
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functor = self._find_flattener(item)
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if not functor:
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raise TypeError("Unknown type requested to flatten: %s (%s)"
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% (item, type(item)))
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self._pre_item_flatten(item)
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graph = functor(item)
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self._post_item_flatten(item, graph)
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return graph
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def _find_flattener(self, item):
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"""Locates the flattening function to use to flatten the given item."""
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if isinstance(item, lf.Flow):
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return self._flatten_linear
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elif isinstance(item, uf.Flow):
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return self._flatten_unordered
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elif isinstance(item, gf.Flow):
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return self._flatten_graph
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elif isinstance(item, task.BaseTask):
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return self._flatten_task
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elif isinstance(item, retry.Retry):
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raise TypeError("Retry controller %s (%s) is used not as a flow "
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"parameter" % (item, type(item)))
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else:
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return None
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def _connect_retry(self, retry, graph):
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graph.add_node(retry)
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# All graph nodes that has not predecessors should be depended on its
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# retry
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for n in gu.get_no_predecessors(graph):
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if n != retry:
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# modified that the same copy isn't modified.
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graph.add_edge(retry, n, FLATTEN_EDGE_DATA.copy())
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# Add link to retry for each node of subgraph that hasn't
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# a parent retry
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for n in graph.nodes_iter():
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if n != retry and 'retry' not in graph.node[n]:
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graph.add_node(n, {'retry': retry})
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def _flatten_linear(self, flow):
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"""Flattens a linear flow."""
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graph = nx.DiGraph(name=flow.name)
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previous_nodes = []
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for item in flow:
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subgraph = self._flatten(item)
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graph = gu.merge_graphs([graph, subgraph])
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# Find nodes that have no predecessor, make them have a predecessor
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# of the previous nodes so that the linearity ordering is
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# maintained. Find the ones with no successors and use this list
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# to connect the next subgraph (if any).
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self._add_new_edges(graph,
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previous_nodes,
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list(gu.get_no_predecessors(subgraph)))
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# There should always be someone without successors, otherwise we
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# have a cycle A -> B -> A situation, which should not be possible.
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previous_nodes = list(gu.get_no_successors(subgraph))
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return graph
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def _flatten_unordered(self, flow):
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"""Flattens a unordered flow."""
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graph = nx.DiGraph(name=flow.name)
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for item in flow:
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# NOTE(harlowja): we do *not* connect the graphs together, this
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# retains that each item (translated to subgraph) is disconnected
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# from each other which will result in unordered execution while
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# running.
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graph = gu.merge_graphs([graph, self._flatten(item)])
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return graph
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def _flatten_task(self, task):
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"""Flattens a individual task."""
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graph = nx.DiGraph(name=task.name)
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graph.add_node(task)
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return graph
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def _flatten_graph(self, flow):
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"""Flattens a graph flow."""
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graph = nx.DiGraph(name=flow.name)
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# Flatten all nodes into a single subgraph per node.
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subgraph_map = {}
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for item in flow:
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subgraph = self._flatten(item)
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subgraph_map[item] = subgraph
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graph = gu.merge_graphs([graph, subgraph])
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# Reconnect all node edges to there corresponding subgraphs.
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for (u, v) in flow.graph.edges_iter():
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# Retain and update the original edge attributes.
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u_v_attrs = gu.get_edge_attrs(flow.graph, u, v)
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# Connect the ones with no predecessors in v to the ones with no
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# successors in u (thus maintaining the edge dependency).
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self._add_new_edges(graph,
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list(gu.get_no_successors(subgraph_map[u])),
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list(gu.get_no_predecessors(subgraph_map[v])),
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edge_attrs=u_v_attrs)
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return graph
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def _pre_item_flatten(self, item):
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"""Called before a item is flattened; any pre-flattening actions."""
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if id(item) in self._history:
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raise ValueError("Already flattened item: %s (%s), recursive"
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" flattening not supported" % (item, id(item)))
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LOG.debug("Starting to flatten '%s'", item)
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self._history.add(id(item))
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def _post_item_flatten(self, item, graph):
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"""Called before a item is flattened; any post-flattening actions."""
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if isinstance(item, flow.Flow) and item.retry:
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self._connect_retry(item.retry, graph)
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LOG.debug("Finished flattening '%s'", item)
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# NOTE(harlowja): this one can be expensive to calculate (especially
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# the cycle detection), so only do it if we know debugging is enabled
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# and not under all cases.
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if LOG.isEnabledFor(logging.DEBUG):
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LOG.debug("Translated '%s' into a graph:", item)
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for line in gu.pformat(graph).splitlines():
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# Indent it so that it's slightly offset from the above line.
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LOG.debug(" %s", line)
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def _pre_flatten(self):
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"""Called before the flattening of the item starts."""
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self._history.clear()
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def _post_flatten(self, graph):
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"""Called after the flattening of the item finishes successfully."""
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dup_names = misc.get_duplicate_keys(graph.nodes_iter(),
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key=lambda node: node.name)
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if dup_names:
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dup_names = ', '.join(sorted(dup_names))
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raise exceptions.InvariantViolation("Tasks with duplicate names "
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"found: %s" % (dup_names))
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self._history.clear()
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@lu.locked
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def flatten(self):
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"""Flattens a item (a task or flow) into a single execution graph."""
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if self._graph is not None:
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return self._graph
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self._pre_flatten()
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graph = self._flatten(self._root)
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self._post_flatten(graph)
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if self._freeze:
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self._graph = nx.freeze(graph)
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else:
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self._graph = graph
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return self._graph
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def flatten(item, freeze=True):
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"""Flattens a item (a task or flow) into a single execution graph."""
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return Flattener(item, freeze=freeze).flatten()
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