53 lines
1.6 KiB
Python
53 lines
1.6 KiB
Python
import functools
|
|
from collections import namedtuple
|
|
|
|
|
|
def make_model_tuple(model):
|
|
"""
|
|
Take a model or a string of the form "app_label.ModelName" and return a
|
|
corresponding ("app_label", "modelname") tuple. If a tuple is passed in,
|
|
assume it's a valid model tuple already and return it unchanged.
|
|
"""
|
|
try:
|
|
if isinstance(model, tuple):
|
|
model_tuple = model
|
|
elif isinstance(model, str):
|
|
app_label, model_name = model.split(".")
|
|
model_tuple = app_label, model_name.lower()
|
|
else:
|
|
model_tuple = model._meta.app_label, model._meta.model_name
|
|
assert len(model_tuple) == 2
|
|
return model_tuple
|
|
except (ValueError, AssertionError):
|
|
raise ValueError(
|
|
"Invalid model reference '%s'. String model references "
|
|
"must be of the form 'app_label.ModelName'." % model
|
|
)
|
|
|
|
|
|
def resolve_callables(mapping):
|
|
"""
|
|
Generate key/value pairs for the given mapping where the values are
|
|
evaluated if they're callable.
|
|
"""
|
|
for k, v in mapping.items():
|
|
yield k, v() if callable(v) else v
|
|
|
|
|
|
def unpickle_named_row(names, values):
|
|
return create_namedtuple_class(*names)(*values)
|
|
|
|
|
|
@functools.lru_cache()
|
|
def create_namedtuple_class(*names):
|
|
# Cache type() with @lru_cache() since it's too slow to be called for every
|
|
# QuerySet evaluation.
|
|
def __reduce__(self):
|
|
return unpickle_named_row, (names, tuple(self))
|
|
|
|
return type(
|
|
'Row',
|
|
(namedtuple('Row', names),),
|
|
{'__reduce__': __reduce__, '__slots__': ()},
|
|
)
|