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)