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import math
import re
import pytest
from dirty_equals import FunctionCheck, HasAttributes, IsInstance
from pydantic_core import CoreConfig, SchemaValidator, ValidationError
from pydantic_core import core_schema as cs
from .conftest import Err, plain_repr
def test_on_field():
v = SchemaValidator(cs.str_schema(min_length=2, max_length=5))
r = plain_repr(v)
assert 'min_length:Some(2)' in r
assert 'max_length:Some(5)' in r
assert v.isinstance_python('test') is True
assert v.isinstance_python('test long') is False
def test_on_config():
v = SchemaValidator(cs.str_schema(), config=CoreConfig(str_max_length=5))
assert 'max_length:Some(5)' in plain_repr(v)
assert v.isinstance_python('test') is True
assert v.isinstance_python('test long') is False
def test_field_priority_arg():
v = SchemaValidator(cs.str_schema(max_length=5), config=CoreConfig(str_max_length=10))
assert 'max_length:Some(5)' in plain_repr(v)
assert v.isinstance_python('test') is True
assert v.isinstance_python('test long') is False
class MyModel:
# this is not required, but it avoids `__pydantic_fields_set__` being included in `__dict__`
__slots__ = '__dict__', '__pydantic_fields_set__', '__pydantic_extra__', '__pydantic_private__'
def test_on_model_class():
v = SchemaValidator(
cs.model_schema(
cls=MyModel,
config=CoreConfig(str_max_length=5),
schema=cs.model_fields_schema(fields={'f': cs.model_field(schema=cs.str_schema())}),
)
)
assert 'max_length:Some(5)' in plain_repr(v)
assert v.isinstance_python({'f': 'test'}) is True
assert v.isinstance_python({'f': 'test long'}) is False
def test_field_priority_model():
v = SchemaValidator(
cs.model_schema(
cls=MyModel,
config=CoreConfig(str_max_length=10),
schema=cs.model_fields_schema(fields={'f': cs.model_field(schema=cs.str_schema(max_length=5))}),
)
)
assert 'max_length:Some(5)' in plain_repr(v)
assert v.isinstance_python({'f': 'test'}) is True
assert v.isinstance_python({'f': 'test long'}) is False
@pytest.mark.parametrize(
'config,float_field_schema,input_value,expected',
[
(
CoreConfig(),
cs.float_schema(),
{'x': 'nan'},
IsInstance(MyModel) & HasAttributes(x=FunctionCheck(math.isnan)),
),
(
CoreConfig(allow_inf_nan=True),
cs.float_schema(),
{'x': 'nan'},
IsInstance(MyModel) & HasAttributes(x=FunctionCheck(math.isnan)),
),
(
CoreConfig(allow_inf_nan=False),
cs.float_schema(),
{'x': 'nan'},
Err('Input should be a finite number [type=finite_number,'),
),
# field `allow_inf_nan` (if set) should have priority over global config
(
CoreConfig(allow_inf_nan=True),
cs.float_schema(allow_inf_nan=False),
{'x': 'nan'},
Err('Input should be a finite number [type=finite_number,'),
),
(
CoreConfig(allow_inf_nan=False),
cs.float_schema(allow_inf_nan=True),
{'x': 'nan'},
IsInstance(MyModel) & HasAttributes(x=FunctionCheck(math.isnan)),
),
],
ids=repr,
)
def test_allow_inf_nan(config: CoreConfig, float_field_schema, input_value, expected):
v = SchemaValidator(
cs.model_schema(
cls=MyModel,
schema=cs.model_fields_schema(fields={'x': cs.model_field(schema=float_field_schema)}),
config=config,
)
)
if isinstance(expected, Err):
with pytest.raises(ValidationError, match=re.escape(expected.message)):
v.validate_python(input_value)
else:
output_dict = v.validate_python(input_value)
assert output_dict == expected
@pytest.mark.parametrize(
'config,input_str',
(
(CoreConfig(), 'type=string_type, input_value=123, input_type=int'),
(CoreConfig(hide_input_in_errors=False), 'type=string_type, input_value=123, input_type=int'),
(CoreConfig(hide_input_in_errors=True), 'type=string_type'),
),
)
def test_hide_input_in_errors(config, input_str):
v = SchemaValidator(
cs.model_schema(
cls=MyModel, schema=cs.model_fields_schema(fields={'f': cs.model_field(schema=cs.str_schema())})
),
config=config,
)
with pytest.raises(ValidationError, match=re.escape(f'Input should be a valid string [{input_str}]')):
assert v.validate_python({'f': 123})
def test_cache_strings():
v = SchemaValidator(cs.str_schema())
assert 'cache_strings=True' in plain_repr(v)
v = SchemaValidator(cs.str_schema(), config=CoreConfig(cache_strings=True))
assert 'cache_strings=True' in plain_repr(v)
v = SchemaValidator(cs.str_schema(), config=CoreConfig(cache_strings=False))
assert 'cache_strings=False' in plain_repr(v)
v = SchemaValidator(cs.str_schema(), config=CoreConfig(cache_strings='keys'))
assert "cache_strings='keys'" in plain_repr(v)
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