def top_sort(fsa: Fsa) -> Fsa:
'''Sort an FSA topologically.
Note:
It returns a new FSA. The input FSA is NOT changed.
Args:
fsa:
The input FSA to be sorted. It can be either a single FSA
or a vector of FSAs.
Returns:
It returns a single FSA if the input is a single FSA; it returns
a vector of FSAs if the input is a vector of FSAs.
'''
need_arc_map = True
ragged_arc, arc_map = _k2.top_sort(fsa.arcs, need_arc_map=need_arc_map)
arc_map = arc_map.to(torch.int64) # required by index_select
sorted_fsa = Fsa.from_ragged_arc(ragged_arc)
for name, value in fsa.named_tensor_attr():
setattr(sorted_fsa, name, value.index_select(0, arc_map))
for name, value in fsa.named_non_tensor_attr():
setattr(sorted_fsa, name, value)
# Delete the properties. Users should set it explicitly
if hasattr(sorted_fsa, 'properties'):
del sorted_fsa.properties
return sorted_fsa
def top_sort(fsa: Fsa) -> Fsa:
'''Sort an FSA topologically.
Note:
It returns a new FSA. The input FSA is NOT changed.
Args:
fsa:
The input FSA to be sorted. It can be either a single FSA
or a vector of FSAs.
Returns:
It returns a single FSA if the input is a single FSA; it returns
a vector of FSAs if the input is a vector of FSAs.
'''
need_arc_map = True
ragged_arc, arc_map = _k2.top_sort(fsa.arcs, need_arc_map=need_arc_map)
out_fsa = k2.utils.fsa_from_unary_function_tensor(fsa, ragged_arc, arc_map)
return out_fsa
def top_sort(fsa: Fsa) -> Fsa:
'''Sort an FSA topologically.
Note:
It returns a new FSA. The input FSA is NOT changed.
Args:
fsa:
The input FSA to be sorted. It can be either a single FSA
or a vector of FSAs.
Returns:
It returns a single FSA if the input is a single FSA; it returns
a vector of FSAs if the input is a vector of FSAs.
'''
need_arc_map = True
ragged_arc, arc_map = _k2.top_sort(fsa.arcs, need_arc_map=need_arc_map)
sorted_fsa = Fsa(ragged_arc)
for name, value in fsa.named_tensor_attr():
setattr(sorted_fsa, name, index_attr(value, arc_map))
for name, value in fsa.named_non_tensor_attr():
setattr(sorted_fsa, name, value)
return sorted_fsa
