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# Authors: R. W. Ford, A. R. Porter, S. Siso, STFC Daresbury Laboratory
# Modified: I. Kavcic, Met Office
# Modified: J. Henrichs, Bureau of Meteorology

'''File containing a PSyclone transformation script for the dynamo0p3
API to apply loop fusion and then OpenMP parallelisation to an invoke
with two Kernels. This can be applied via the -s option in the
generator.py script.'''

from psyclone.configuration import Config
from psyclone.domain.lfric.transformations import LFRicLoopFuseTrans
from psyclone.transformations import OMPParallelTrans, Dynamo0p3OMPLoopTrans


def trans(psy):
    ''' PSyclone transformation script for the dynamo0p3 API to apply
    loop fusion and OpenMP for a particular example.'''
    otrans = OMPParallelTrans()
    ltrans = Dynamo0p3OMPLoopTrans()
    ftrans = LFRicLoopFuseTrans()

    invoke = psy.invokes.invoke_list[0]
    schedule = invoke.schedule

    config = Config.get()
    if config.api_conf("dynamo0.3").compute_annexed_dofs and \
       config.distributed_memory:
        # We can't loop fuse as the loop bounds differ so add
        # OpenMP parallel do directives to the loops
        otrans.apply(schedule.children[0])
        otrans.apply(schedule.children[1])
    else:
        # Loop fuse the two built-in kernels. The 'same_space' flag needs to
        # be set as built-ins are over ANY_SPACE.
        ftrans.apply(schedule[0], schedule[1], {"same_space": True})

        # Add an OpenMP do directive to the resultant loop-fused loop,
        # specifying that we want reproducible reductions
        ltrans.apply(schedule.children[0], {"reprod": True})

        # Add an OpenMP parallel directive around the OpenMP do directive
        otrans.apply(schedule.children[0])

    # take a look at what we've done
    print(schedule.view())
    schedule.dag()

    return psy
