1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
|
import typing
import pytest
from pydantic_core import SchemaError, SchemaValidator, ValidationError, core_schema
from pydantic_core import core_schema as cs
class Foo:
pass
class Bar(Foo):
pass
class Spam:
pass
def test_validate_json() -> None:
v = SchemaValidator(cs.is_instance_schema(cls=Foo))
with pytest.raises(ValidationError) as exc_info:
v.validate_json('"foo"')
assert exc_info.value.errors()[0]['type'] == 'needs_python_object'
def test_is_instance():
v = SchemaValidator(cs.is_instance_schema(cls=Foo))
foo = Foo()
assert v.validate_python(foo) == foo
assert v.isinstance_python(foo) is True
bar = Bar()
assert v.validate_python(bar) == bar
s = Spam()
assert v.isinstance_python(s) is False
with pytest.raises(ValidationError) as exc_info:
v.validate_python(s)
assert exc_info.value.errors(include_url=False) == [
{
'type': 'is_instance_of',
'loc': (),
'msg': 'Input should be an instance of Foo',
'input': s,
'ctx': {'class': 'Foo'},
}
]
with pytest.raises(ValidationError, match='type=is_instance_of'):
v.validate_python(Foo)
@pytest.mark.parametrize(
'schema_class,input_val,value',
[
(Foo, Foo(), True),
(Foo, Foo, False),
(Foo, Bar(), True),
(Foo, Bar, False),
(Bar, Foo(), False),
(Bar, Foo, False),
(dict, {1: 2}, True),
(dict, {1, 2}, False),
(type, Foo, True),
(type, Foo(), False),
],
)
def test_is_instance_cases(schema_class, input_val, value):
v = SchemaValidator(cs.is_instance_schema(cls=schema_class))
assert v.isinstance_python(input_val) == value
@pytest.mark.parametrize('input_cls', [123, 'foo', Foo(), [], {1: 2}])
def test_is_instance_invalid(input_cls):
with pytest.raises(SchemaError, match="SchemaError: 'cls' must be valid as the first argument to 'isinstance'"):
SchemaValidator(cs.is_instance_schema(cls=input_cls))
class HasIsInstanceMeta(type):
def __instancecheck__(self, instance) -> bool:
if 'error' in repr(instance):
# an error here comes from a problem in the schema, not in the input value, so raise as internal error
raise TypeError('intentional error')
return 'true' in repr(instance)
class HasIsInstance(metaclass=HasIsInstanceMeta):
pass
def test_instancecheck():
v = SchemaValidator(cs.is_instance_schema(cls=HasIsInstance))
assert v.validate_python('true') == 'true'
with pytest.raises(ValidationError, match='type=is_instance_of'):
v.validate_python('other')
with pytest.raises(TypeError, match='intentional error'):
v.validate_python('error')
def test_repr():
v = SchemaValidator(cs.union_schema(choices=[cs.int_schema(), cs.is_instance_schema(cls=Foo)]))
assert v.isinstance_python(4) is True
assert v.isinstance_python(Bar()) is True
assert v.isinstance_python('foo') is False
with pytest.raises(ValidationError, match=r'is-instance\[Foo\]\s+Input should be an instance of Foo'):
v.validate_python('foo')
@pytest.mark.parametrize(
'input_val,value',
[
(Foo, True),
(Foo(), False),
(str, True),
('foo', False),
(int, True),
(1, False),
(type, True),
(type('Foobar', (), {'x': 1}), True),
],
)
def test_is_type(input_val, value):
v = SchemaValidator(cs.is_instance_schema(cls=type))
assert v.isinstance_python(input_val) == value
def test_is_instance_dict():
v = SchemaValidator(
core_schema.dict_schema(
keys_schema=core_schema.is_instance_schema(str), values_schema=core_schema.is_instance_schema(int)
)
)
assert v.isinstance_python({'foo': 1}) is True
assert v.isinstance_python({1: 1}) is False
def test_is_instance_dict_not_str():
v = SchemaValidator(core_schema.dict_schema(keys_schema=core_schema.is_instance_schema(int)))
assert v.isinstance_python({1: 1}) is True
assert v.isinstance_python({'foo': 1}) is False
def test_is_instance_sequence():
v = SchemaValidator(core_schema.is_instance_schema(typing.Sequence))
assert v.isinstance_python(1) is False
assert v.isinstance_python([1]) is True
with pytest.raises(ValidationError, match=r'Input should be an instance of typing.Sequence \[type=is_instance_of,'):
v.validate_python(1)
def test_is_instance_tuple():
v = SchemaValidator(core_schema.is_instance_schema((int, str)))
assert v.isinstance_python(1) is True
assert v.isinstance_python('foobar') is True
assert v.isinstance_python([1]) is False
with pytest.raises(ValidationError, match=r"Input should be an instance of \(<class 'int'>, <class 'str'>\)"):
v.validate_python([1])
def test_class_repr():
v = SchemaValidator(core_schema.is_instance_schema(int, cls_repr='Foobar'))
assert v.validate_python(1) == 1
with pytest.raises(ValidationError, match=r'Input should be an instance of Foobar \[type=is_instance_of,'):
v.validate_python('1')
def test_is_instance_json_type_before_validator():
# See https://github.com/pydantic/pydantic/issues/6573 - when using a
# "before" validator to coerce JSON to valid Python input it should be
# possible to use isinstance validation. This gives a way for things
# such as type to have a valid input from JSON.
schema = core_schema.is_instance_schema(type)
v = SchemaValidator(schema)
with pytest.raises(ValidationError) as exc_info:
v.validate_json('null')
assert exc_info.value.errors()[0]['type'] == 'needs_python_object'
# now wrap in a before validator
def set_type_to_int(input: None) -> type:
return int
schema = core_schema.no_info_before_validator_function(set_type_to_int, schema)
v = SchemaValidator(schema)
assert v.validate_json('null') == int
|
