Python hypothesis.strategies 模块，tuples() 实例源码

def _create_hyp_class(attrs_and_strategy):
    """
    A helper function for Hypothesis to generate attrs classes.

    The result is a tuple: an attrs class, and a tuple of values to
    instantiate it.
    """
    def key(t):
        return t[0].default is not NOTHING
    attrs_and_strat = sorted(attrs_and_strategy, key=key)
    attrs = [a[0] for a in attrs_and_strat]
    for i, a in enumerate(attrs):
        a.counter = i
    vals = tuple((a[1]) for a in attrs_and_strat)
    return st.tuples(
        st.just(make_class('HypClass',
                           OrderedDict(zip(gen_attr_names(), attrs)))),
        st.tuples(*vals))

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 as an attribute:
        * just the simpler class
        * a list of simpler classes
        * a dict mapping the string "cls" to a simpler class.
    """
    # A strategy producing tuples of the form ([list of attributes], <given
    # class strategy>).
    attrs_and_classes = st.tuples(lists_of_attrs(defaults=True),
                                  simple_class_strategy)

    return (attrs_and_classes.flatmap(just_class) |
            attrs_and_classes.flatmap(list_of_class) |
            attrs_and_classes.flatmap(dict_of_class))

def pattern_to_statements(pattern):
    if isinstance(pattern, template):
        return lists(just(pattern), min_size=1, max_size=1)
    rule, value = pattern
    if rule == 'sequence':
        return tuples(*map(pattern_to_statements, value)).map(unpack_list).map(list)
    elif rule == 'alternates':
        return one_of(*map(pattern_to_statements, value))
    elif rule == 'zeroOrMore':
        return lists(pattern_to_statements(value)).map(unpack_list).map(list)
    elif rule == 'oneOrMore':
        return lists(pattern_to_statements(value), min_size=1).map(unpack_list).map(list)
    elif rule == 'optional':
        return lists(pattern_to_statements(value), min_size=0, max_size=1).map(unpack_list).map(list)
    else:
        raise Exception("impossible!", rule)



# this replicates the current scorm pattern, a realistic example of medium
# complexity. Note it has repeated elements, just not in ambiguous ways.

def random_reference(draw, prefix = random_prefix()):
    number = st.integers(min_value = 0)
    return draw(st.tuples(prefix, number))

def add_bools(list_of_lists):
    """
    Given recursive list that can contain other lists, return tuple of that plus
    a booleans strategy for each list.
    """
    l = []
    def count(recursive):
        l.append(1)
        for child in recursive:
            if isinstance(child, list):
                count(child)
    count(list_of_lists)
    return st.tuples(st.just(list_of_lists), st.tuples(*[st.sampled_from([True, False]) for i in l]))

def remove_strategy(self):
        def get_address(service_name):
            return st.tuples(st.just(service_name), st.sampled_from(
                self.fake.services[service_name]))
        return st.tuples(st.just("remove"), (
                st.sampled_from(list(self.fake.services.keys())).flatmap(get_address)))

def steps(self):
        result = add_strategy | replace_strategy
        # Replace or add to a known service cluster:
        if self.fake.services:
            result |= st.tuples(st.just("replace"),
                                st.tuples(st.sampled_from(list(self.fake.services.keys())),
                                          st.lists(nice_strings)))
            result |= st.tuples(st.just("add"),
                                st.tuples(st.sampled_from(list(self.fake.services.keys())),
                                          nice_strings))
        # Remove a known address from known cluster:
        if not self.fake.is_empty():
            result |= self.remove_strategy()
        return result

def lists_of_primitives(draw):
    """Generate a strategy that yields tuples of list of primitives and types.

    For example, a sample value might be ([1,2], List[int]).
    """
    prim_strat, t = draw(primitive_strategies)
    list_t = draw(list_types.map(lambda list_t: list_t[t]) | list_types)
    return draw(st.lists(prim_strat)), list_t

def create_dict_and_type(tuple_of_strats):
    """Map two primitive strategies into a strategy for dict and type."""
    (prim_strat_1, type_1), (prim_strat_2, type_2) = tuple_of_strats

    return st.tuples(st.dictionaries(prim_strat_1, prim_strat_2),
                     create_generic_dict_type(type_1, type_2))

def simple_classes(defaults=None):
    """
    Return a strategy that yields tuples of simple classes and values to
    instantiate them.
    """
    return lists_of_attrs(defaults).flatmap(_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).