def __str__(self) -> str:
'''Return a string representation of this object
For visualization and debug only.
'''
if hasattr(self, 'aux_labels') and isinstance(self.aux_labels,
torch.Tensor):
aux_labels = self.aux_labels.to(torch.int32)
else:
aux_labels = None
if self.arcs.num_axes() == 2:
ans = 'k2.Fsa: ' + _k2.fsa_to_str(self.arcs, False, aux_labels)
else:
ans = 'k2.FsaVec: \n'
for i in range(self.shape[0]):
# get the i-th Fsa
ragged_arc, start = self.arcs.index(0, i)
end = start + ragged_arc.values().shape[0]
ans += 'FsaVec[' + str(i) + ']: ' + _k2.fsa_to_str(
ragged_arc, False,
None if aux_labels is None else aux_labels[start:end])
ans += 'properties_str = ' + _k2.fsa_properties_as_str(
self._properties) + '.'
for name, value in self.named_tensor_attr(include_scores=False):
sep = '\n'
ans += f'{sep}{name}: {value}'
for name, value in self.named_non_tensor_attr():
if name == 'symbols':
continue
sep = '\n'
ans += f'{sep}{name}: {value}'
return ans
def to_str(fsa: Fsa, openfst: bool = False) -> str:
'''Convert an Fsa to a string. This version prints out all integer
labels and integer ragged labels on the same line as each arc, the
same format accepted by Fsa.from_str().
Note:
The returned string can be used to construct an Fsa with Fsa.from_str(),
but you would need to know the names of the auxiliary labels and ragged
labels.
Args:
openfst:
Optional. If true, we negate the scores during the conversion.
Returns:
A string representation of the Fsa.
'''
assert fsa.arcs.num_axes() == 2
extra_labels = []
ragged_labels = []
for name, value in sorted(fsa.named_tensor_attr(include_scores=False)):
if isinstance(value, torch.Tensor) and value.dtype == torch.int32:
extra_labels.append(value)
elif isinstance(value, k2.RaggedTensor):
ragged_labels.append(value)
return _k2.fsa_to_str(fsa.arcs,
openfst=openfst,
extra_labels=extra_labels,
ragged_labels=ragged_labels)
def to_str(fsa: Fsa, openfst: bool = False) -> str:
'''Convert an Fsa to a string.
Note:
The returned string can be used to construct an Fsa.
Args:
openfst:
Optional. If true, we negate the scores during the conversion,
Returns:
A string representation of the Fsa.
'''
if hasattr(fsa, 'aux_labels'):
aux_labels = fsa.aux_labels.to(torch.int32)
else:
aux_labels = None
return _k2.fsa_to_str(fsa.arcs, openfst, aux_labels)
def to_str(fsa: Fsa, openfst: bool = False) -> str:
'''Convert an Fsa to a string.
Caution:
If the input fsa's aux_labels is a ragged tensor, it is ignored.
Note:
The returned string can be used to construct an Fsa.
Args:
openfst:
Optional. If true, we negate the scores during the conversion,
Returns:
A string representation of the Fsa.
'''
if hasattr(fsa, 'aux_labels') and isinstance(fsa.aux_labels, torch.Tensor):
aux_labels = fsa.aux_labels.to(torch.int32)
else:
aux_labels = None
return _k2.fsa_to_str(fsa.arcs, openfst, aux_labels)
def to_str_simple(fsa: Fsa, openfst: bool = False) -> str:
'''Convert an Fsa to a string. This is less complete than Fsa.to_str(),
fsa.__str__(), or to_str_full(), meaning it prints only fsa.aux_labels and
no ragged labels, not printing any other attributes. This is used in
testing.
Note:
The returned string can be used to construct an Fsa. See also to_str().
Args:
openfst:
Optional. If true, we negate the scores during the conversion.
Returns:
A string representation of the Fsa.
'''
assert fsa.arcs.num_axes() == 2
if hasattr(fsa, 'aux_labels') and isinstance(fsa.aux_labels, torch.Tensor):
aux_labels = [fsa.aux_labels.to(torch.int32)]
else:
aux_labels = []
return _k2.fsa_to_str(fsa.arcs, openfst, aux_labels, [])