attrs/tests/__init__.py

124 lines
3.8 KiB
Python

from __future__ import absolute_import, division, print_function
import keyword
import string
from hypothesis import strategies as st
import attr
from attr import Attribute
from attr._make import NOTHING, make_class
def simple_class(cmp=False, repr=False, hash=False, slots=False):
"""
Return a new simple class.
"""
return make_class(
"C", ["a", "b"],
cmp=cmp, repr=repr, hash=hash, init=True, slots=slots
)
def simple_attr(name, default=NOTHING, validator=None, repr=True,
cmp=True, hash=True, init=True):
"""
Return an attribute with a name and no other bells and whistles.
"""
return Attribute(
name=name, default=default, validator=validator, repr=repr,
cmp=cmp, hash=hash, init=init
)
class TestSimpleClass(object):
"""
Tests for the testing helper function `make_class`.
"""
def test_returns_class(self):
"""
Returns a class object.
"""
assert type is simple_class().__class__
def returns_distinct_classes(self):
"""
Each call returns a completely new class.
"""
assert simple_class() is not simple_class()
def _gen_attr_names():
"""
Generate names for attributes, 'a'...'z', then 'aa'...'zz'.
~702 different attribute names should be enough in practice.
Some short strings (such as 'as') are keywords, so we skip them.
"""
lc = string.ascii_lowercase
for c in lc:
yield c
for outer in lc:
for inner in lc:
res = outer + inner
if keyword.iskeyword(res):
continue
yield outer + inner
def _create_hyp_class(attrs):
"""
A helper function for Hypothesis to generate attrs classes.
"""
return make_class('HypClass', dict(zip(_gen_attr_names(), attrs)))
def _create_hyp_nested_strategy(simple_class_strategy):
"""
Create a recursive attrs class.
Given a strategy for building (simpler) classes, create and return
a strategy for building classes that have the simpler class as an
attribute.
"""
# Use a tuple strategy to combine simple attributes and an attr class.
def just_class(tup):
combined_attrs = list(tup[0])
combined_attrs.append(attr.ib(default=attr.Factory(tup[1])))
return _create_hyp_class(combined_attrs)
def list_of_class(tup):
default = attr.Factory(lambda: [tup[1]()])
combined_attrs = list(tup[0])
combined_attrs.append(attr.ib(default=default))
return _create_hyp_class(combined_attrs)
def dict_of_class(tup):
default = attr.Factory(lambda: {"cls": tup[1]()})
combined_attrs = list(tup[0])
combined_attrs.append(attr.ib(default=default))
return _create_hyp_class(combined_attrs)
return st.one_of(st.tuples(st.lists(simple_attrs), simple_class_strategy)
.map(just_class),
st.tuples(st.lists(simple_attrs), simple_class_strategy)
.map(list_of_class))
bare_attrs = st.just(attr.ib(default=None))
int_attrs = st.integers().map(lambda i: attr.ib(default=i))
str_attrs = st.text().map(lambda s: attr.ib(default=s))
float_attrs = st.floats().map(lambda f: attr.ib(default=f))
dict_attrs = (st.dictionaries(keys=st.text(), values=st.integers())
.map(lambda d: attr.ib(default=d)))
simple_attrs = st.one_of(bare_attrs, int_attrs, str_attrs, float_attrs,
dict_attrs)
simple_classes = st.lists(simple_attrs).map(_create_hyp_class)
# Ok, so st.recursive works by taking a base strategy (in this case,
# simple_classes) and a special function. This function receives a strategy,
# and returns another strategy (building on top of the base strategy).
nested_classes = st.recursive(simple_classes, _create_hyp_nested_strategy)