def _process(typeinfo: typecheck.Type, val: typing.Any, make: bool) -> typing.Any:
if val is None:
return None
elif make and hasattr(typeinfo, "from_state"):
return typeinfo.from_state(val)
elif not make and hasattr(val, "get_state"):
return val.get_state()
typename = str(typeinfo)
if typename.startswith("typing.List"):
T = typecheck.sequence_type(typeinfo)
return [_process(T, x, make) for x in val]
elif typename.startswith("typing.Tuple"):
Ts = typecheck.tuple_types(typeinfo)
if len(Ts) != len(val):
raise ValueError("Invalid data. Expected {}, got {}.".format(Ts, val))
return tuple(
_process(T, x, make) for T, x in zip(Ts, val)
)
elif typename.startswith("typing.Dict"):
k_cls, v_cls = typecheck.mapping_types(typeinfo)
return {
_process(k_cls, k, make): _process(v_cls, v, make)
for k, v in val.items()
}
elif typename.startswith("typing.Any"):
# FIXME: Remove this when we remove flow.metadata
assert isinstance(val, (int, str, bool, bytes))
return val
else:
return typeinfo(val)
def _process(typeinfo: typecheck.Type, val: typing.Any,
make: bool) -> typing.Any:
if val is None:
return None
elif make and hasattr(typeinfo, "from_state"):
return typeinfo.from_state(val)
elif not make and hasattr(val, "get_state"):
return val.get_state()
typename = str(typeinfo)
if typename.startswith("typing.List"):
T = typecheck.sequence_type(typeinfo)
return [_process(T, x, make) for x in val]
elif typename.startswith("typing.Tuple"):
Ts = typecheck.tuple_types(typeinfo)
if len(Ts) != len(val):
raise ValueError("Invalid data. Expected {}, got {}.".format(
Ts, val))
return tuple(_process(T, x, make) for T, x in zip(Ts, val))
elif typename.startswith("typing.Dict"):
k_cls, v_cls = typecheck.mapping_types(typeinfo)
return {
_process(k_cls, k, make): _process(v_cls, v, make)
for k, v in val.items()
}
elif typename.startswith("typing.Any"):
# FIXME: Remove this when we remove flow.metadata
assert isinstance(val, (int, str, bool, bytes))
return val
else:
return typeinfo(val)
def _process(typeinfo: typecheck.Type, val: typing.Any,
make: bool) -> typing.Any:
if val is None:
return None
elif make and hasattr(typeinfo, "from_state"):
return typeinfo.from_state(val)
elif not make and hasattr(val, "get_state"):
return val.get_state()
typename = str(typeinfo)
if typename.startswith("typing.List"):
T = typecheck.sequence_type(typeinfo)
return [_process(T, x, make) for x in val]
elif typename.startswith("typing.Tuple"):
Ts = typecheck.tuple_types(typeinfo)
if len(Ts) != len(val):
raise ValueError("Invalid data. Expected {}, got {}.".format(
Ts, val))
return tuple(_process(T, x, make) for T, x in zip(Ts, val))
elif typename.startswith("typing.Dict"):
k_cls, v_cls = typecheck.mapping_types(typeinfo)
return {
_process(k_cls, k, make): _process(v_cls, v, make)
for k, v in val.items()
}
elif typename.startswith("typing.Any"):
# This requires a bit of explanation. We can't import our IO layer here,
# because it causes a circular import. Rather than restructuring the
# code for this, we use JSON serialization, which has similar primitive
# type restrictions as tnetstring, to check for conformance.
try:
json.dumps(val)
except TypeError:
raise ValueError(f"Data not serializable: {val}")
return val
else:
return typeinfo(val)
def _process(typeinfo: typecheck.Type, val: typing.Any, make: bool) -> typing.Any:
if val is None:
return None
elif make and hasattr(typeinfo, "from_state"):
return typeinfo.from_state(val)
elif not make and hasattr(val, "get_state"):
return val.get_state()
typename = str(typeinfo)
if typename.startswith("typing.List"):
T = typecheck.sequence_type(typeinfo)
return [_process(T, x, make) for x in val]
elif typename.startswith("typing.Tuple"):
Ts = typecheck.tuple_types(typeinfo)
if len(Ts) != len(val):
raise ValueError("Invalid data. Expected {}, got {}.".format(Ts, val))
return tuple(
_process(T, x, make) for T, x in zip(Ts, val)
)
elif typename.startswith("typing.Dict"):
k_cls, v_cls = typecheck.mapping_types(typeinfo)
return {
_process(k_cls, k, make): _process(v_cls, v, make)
for k, v in val.items()
}
elif typename.startswith("typing.Any"):
# This requires a bit of explanation. We can't import our IO layer here,
# because it causes a circular import. Rather than restructuring the
# code for this, we use JSON serialization, which has similar primitive
# type restrictions as tnetstring, to check for conformance.
try:
json.dumps(val)
except TypeError:
raise ValueError(f"Data not serializable: {val}")
return val
else:
return typeinfo(val)