import logging
import os
import sys

logging.basicConfig(level=logging.ERROR)

top_dir = os.path.abspath(os.path.join(os.path.dirname(__file__),
                                       os.pardir,
                                       os.pardir))
sys.path.insert(0, top_dir)

import taskflow.engines

from taskflow.patterns import linear_flow as lf
from taskflow.patterns import unordered_flow as uf
from taskflow import task

# This examples shows how LinearFlow and ParallelFlow can be used
# together to execute calculations in parallel and then use the
# result for the next task. Adder task is used for all calculations
# and arguments' bindings are used to set correct parameters to the task.


class Provider(task.Task):
    def __init__(self, name, *args, **kwargs):
        super(Provider, self).__init__(name=name, **kwargs)
        self._provide = args

    def execute(self):
        return self._provide


class Adder(task.Task):
    def execute(self, x, y):
        return x + y


flow = lf.Flow('root').add(
    # x1 = 2, y1 = 3, x2 = 5, x3 = 8
    Provider("provide-adder", 2, 3, 5, 8,
             provides=('x1', 'y1', 'x2', 'y2')),
    uf.Flow('adders').add(
        # z1 = x1+y1 = 5
        Adder(name="add", provides='z1', rebind=['x1', 'y1']),
        # z2 = x2+y2 = 13
        Adder(name="add-2", provides='z2', rebind=['x2', 'y2']),
    ),
    # r = z1+z2 = 18
    Adder(name="sum-1", provides='r', rebind=['z1', 'z2']))


result = taskflow.engines.run(flow, engine_conf='parallel')
print result