Coverage for pydantic/_internal/_known_annotated_metadata.py: 91.15%

1from __future__ import annotations 39 ctx1arbsctduevfghwixjykzlAGHIJKLMmBnCoDpEqF

2 

3from collections import defaultdict 39 ctx1arbsctduevfghwixjykzlAGHIJKLMmBnCoDpEqF

4from collections.abc import Iterable 39 ctx1arbsctduevfghwixjykzlAGHIJKLMmBnCoDpEqF

5from copy import copy 39 ctx1arbsctduevfghwixjykzlAGHIJKLMmBnCoDpEqF

6from decimal import Decimal 39 ctx1arbsctduevfghwixjykzlAGHIJKLMmBnCoDpEqF

7from functools import lru_cache, partial 39 ctx1arbsctduevfghwixjykzlAGHIJKLMmBnCoDpEqF

8from typing import TYPE_CHECKING, Any 39 ctx1arbsctduevfghwixjykzlAGHIJKLMmBnCoDpEqF

9 

10from pydantic_core import CoreSchema, PydanticCustomError, ValidationError, to_jsonable_python 39 ctx1arbsctduevfghwixjykzlAGHIJKLMmBnCoDpEqF

11from pydantic_core import core_schema as cs 39 ctx1arbsctduevfghwixjykzlAGHIJKLMmBnCoDpEqF

12 

13from ._fields import PydanticMetadata 39 ctx1arbsctduevfghwixjykzlAGHIJKLMmBnCoDpEqF

14from ._import_utils import import_cached_field_info 39 ctx1arbsctduevfghwixjykzlAGHIJKLMmBnCoDpEqF

15 

16if TYPE_CHECKING: 39 ctx1arbsctduevfghwixjykzlAGHIJKLMmBnCoDpEqF

17 pass 

18 

19STRICT = {'strict'} 39 ctx1arbsctduevfghwixjykzlAGHIJKLMmBnCoDpEqF

20FAIL_FAST = {'fail_fast'} 39 ctx1arbsctduevfghwixjykzlAGHIJKLMmBnCoDpEqF

21LENGTH_CONSTRAINTS = {'min_length', 'max_length'} 39 ctx1arbsctduevfghwixjykzlAGHIJKLMmBnCoDpEqF

22INEQUALITY = {'le', 'ge', 'lt', 'gt'} 39 ctx1arbsctduevfghwixjykzlAGHIJKLMmBnCoDpEqF

23NUMERIC_CONSTRAINTS = {'multiple_of', *INEQUALITY} 39 ctx1arbsctduevfghwixjykzlAGHIJKLMmBnCoDpEqF

24ALLOW_INF_NAN = {'allow_inf_nan'} 39 ctx1arbsctduevfghwixjykzlAGHIJKLMmBnCoDpEqF

25 

26STR_CONSTRAINTS = { 39 ctx1arbsctduevfghwixjykzlAGHIJKLMmBnCoDpEqF

27 *LENGTH_CONSTRAINTS, 

28 *STRICT, 

29 'strip_whitespace', 

30 'to_lower', 

31 'to_upper', 

32 'pattern', 

33 'coerce_numbers_to_str', 

34} 

35BYTES_CONSTRAINTS = {*LENGTH_CONSTRAINTS, *STRICT} 39 ctx1arbsctduevfghwixjykzlAGHIJKLMmBnCoDpEqF

36 

37LIST_CONSTRAINTS = {*LENGTH_CONSTRAINTS, *STRICT, *FAIL_FAST} 39 ctx1arbsctduevfghwixjykzlAGHIJKLMmBnCoDpEqF

38TUPLE_CONSTRAINTS = {*LENGTH_CONSTRAINTS, *STRICT, *FAIL_FAST} 39 ctx1arbsctduevfghwixjykzlAGHIJKLMmBnCoDpEqF

39SET_CONSTRAINTS = {*LENGTH_CONSTRAINTS, *STRICT, *FAIL_FAST} 39 ctx1arbsctduevfghwixjykzlAGHIJKLMmBnCoDpEqF

40DICT_CONSTRAINTS = {*LENGTH_CONSTRAINTS, *STRICT} 39 ctx1arbsctduevfghwixjykzlAGHIJKLMmBnCoDpEqF

41GENERATOR_CONSTRAINTS = {*LENGTH_CONSTRAINTS, *STRICT} 39 ctx1arbsctduevfghwixjykzlAGHIJKLMmBnCoDpEqF

42SEQUENCE_CONSTRAINTS = {*LENGTH_CONSTRAINTS, *FAIL_FAST} 39 ctx1arbsctduevfghwixjykzlAGHIJKLMmBnCoDpEqF

43 

44FLOAT_CONSTRAINTS = {*NUMERIC_CONSTRAINTS, *ALLOW_INF_NAN, *STRICT} 39 ctx1arbsctduevfghwixjykzlAGHIJKLMmBnCoDpEqF

45DECIMAL_CONSTRAINTS = {'max_digits', 'decimal_places', *FLOAT_CONSTRAINTS} 39 ctx1arbsctduevfghwixjykzlAGHIJKLMmBnCoDpEqF

46INT_CONSTRAINTS = {*NUMERIC_CONSTRAINTS, *ALLOW_INF_NAN, *STRICT} 39 ctx1arbsctduevfghwixjykzlAGHIJKLMmBnCoDpEqF

47BOOL_CONSTRAINTS = STRICT 39 ctx1arbsctduevfghwixjykzlAGHIJKLMmBnCoDpEqF

48UUID_CONSTRAINTS = STRICT 39 ctx1arbsctduevfghwixjykzlAGHIJKLMmBnCoDpEqF

49 

50DATE_TIME_CONSTRAINTS = {*NUMERIC_CONSTRAINTS, *STRICT} 39 ctx1arbsctduevfghwixjykzlAGHIJKLMmBnCoDpEqF

51TIMEDELTA_CONSTRAINTS = {*NUMERIC_CONSTRAINTS, *STRICT} 39 ctx1arbsctduevfghwixjykzlAGHIJKLMmBnCoDpEqF

52TIME_CONSTRAINTS = {*NUMERIC_CONSTRAINTS, *STRICT} 39 ctx1arbsctduevfghwixjykzlAGHIJKLMmBnCoDpEqF

53LAX_OR_STRICT_CONSTRAINTS = STRICT 39 ctx1arbsctduevfghwixjykzlAGHIJKLMmBnCoDpEqF

54ENUM_CONSTRAINTS = STRICT 39 ctx1arbsctduevfghwixjykzlAGHIJKLMmBnCoDpEqF

55COMPLEX_CONSTRAINTS = STRICT 39 ctx1arbsctduevfghwixjykzlAGHIJKLMmBnCoDpEqF

56 

57UNION_CONSTRAINTS = {'union_mode'} 39 ctx1arbsctduevfghwixjykzlAGHIJKLMmBnCoDpEqF

58URL_CONSTRAINTS = { 39 ctx1arbsctduevfghwixjykzlAGHIJKLMmBnCoDpEqF

59 'max_length', 

60 'allowed_schemes', 

61 'host_required', 

62 'default_host', 

63 'default_port', 

64 'default_path', 

65} 

66 

67TEXT_SCHEMA_TYPES = ('str', 'bytes', 'url', 'multi-host-url') 39 ctx1arbsctduevfghwixjykzlAGHIJKLMmBnCoDpEqF

68SEQUENCE_SCHEMA_TYPES = ('list', 'tuple', 'set', 'frozenset', 'generator', *TEXT_SCHEMA_TYPES) 39 ctx1arbsctduevfghwixjykzlAGHIJKLMmBnCoDpEqF

69NUMERIC_SCHEMA_TYPES = ('float', 'int', 'date', 'time', 'timedelta', 'datetime') 39 ctx1arbsctduevfghwixjykzlAGHIJKLMmBnCoDpEqF

70 

71CONSTRAINTS_TO_ALLOWED_SCHEMAS: dict[str, set[str]] = defaultdict(set) 39 ctx1arbsctduevfghwixjykzlAGHIJKLMmBnCoDpEqF

72 

73constraint_schema_pairings: list[tuple[set[str], tuple[str, ...]]] = [ 39 ctx1arbsctduevfghwixjykzlAGHIJKLMmBnCoDpEqF

74 (STR_CONSTRAINTS, TEXT_SCHEMA_TYPES), 

75 (BYTES_CONSTRAINTS, ('bytes',)), 

76 (LIST_CONSTRAINTS, ('list',)), 

77 (TUPLE_CONSTRAINTS, ('tuple',)), 

78 (SET_CONSTRAINTS, ('set', 'frozenset')), 

79 (DICT_CONSTRAINTS, ('dict',)), 

80 (GENERATOR_CONSTRAINTS, ('generator',)), 

81 (FLOAT_CONSTRAINTS, ('float',)), 

82 (INT_CONSTRAINTS, ('int',)), 

83 (DATE_TIME_CONSTRAINTS, ('date', 'time', 'datetime', 'timedelta')), 

84 # TODO: this is a bit redundant, we could probably avoid some of these 

85 (STRICT, (*TEXT_SCHEMA_TYPES, *SEQUENCE_SCHEMA_TYPES, *NUMERIC_SCHEMA_TYPES, 'typed-dict', 'model')), 

86 (UNION_CONSTRAINTS, ('union',)), 

87 (URL_CONSTRAINTS, ('url', 'multi-host-url')), 

88 (BOOL_CONSTRAINTS, ('bool',)), 

89 (UUID_CONSTRAINTS, ('uuid',)), 

90 (LAX_OR_STRICT_CONSTRAINTS, ('lax-or-strict',)), 

91 (ENUM_CONSTRAINTS, ('enum',)), 

92 (DECIMAL_CONSTRAINTS, ('decimal',)), 

93 (COMPLEX_CONSTRAINTS, ('complex',)), 

94] 

95 

96for constraints, schemas in constraint_schema_pairings: 39 ctx1arbsctduevfghwixjykzlAGHIJKLMmBnCoDpEqF

97 for c in constraints: 39 ctx1arbsctduevfghwixjykzlAGHIJKLMmBnCoDpEqF

98 CONSTRAINTS_TO_ALLOWED_SCHEMAS[c].update(schemas) 39 ctx1arbsctduevfghwixjykzlAGHIJKLMmBnCoDpEqF

99 

100 

101def as_jsonable_value(v: Any) -> Any: 39 ctx1arbsctduevfghwixjykzlAGHIJKLMmBnCoDpEqF

102 if type(v) not in (int, str, float, bytes, bool, type(None)): 32 ctx1arbsctduevfghwixjykzlAmBnCoDpEqF

103 return to_jsonable_python(v) 32 ctx1arbsctduevfghwixjykzlAmBnCoDpEqF

104 return v 32 ctx1arbsctduevfghwixjykzlAmBnCoDpEqF

105 

106 

107def expand_grouped_metadata(annotations: Iterable[Any]) -> Iterable[Any]: 39 ctx1arbsctduevfghwixjykzlAGHIJKLMmBnCoDpEqF

108 """Expand the annotations. 

109 

110 Args: 

111 annotations: An iterable of annotations. 

112 

113 Returns: 

114 An iterable of expanded annotations. 

115 

116 Example: 

117 ```python 

118 from annotated_types import Ge, Len 

119 

120 from pydantic._internal._known_annotated_metadata import expand_grouped_metadata 

121 

122 print(list(expand_grouped_metadata([Ge(4), Len(5)]))) 

123 #> [Ge(ge=4), MinLen(min_length=5)] 

124 ``` 

125 """ 

126 import annotated_types as at 39 ctx1arbsctduevfghwixjykzlAGHIJKLMmBnCoDpEqF

127 

128 FieldInfo = import_cached_field_info() 39 ctx1arbsctduevfghwixjykzlAGHIJKLMmBnCoDpEqF

129 

130 for annotation in annotations: 39 ctx1arbsctduevfghwixjykzlAGHIJKLMmBnCoDpEqF

131 if isinstance(annotation, at.GroupedMetadata): 39 ctx1arbsctduevfghwixjykzlAGHIJKLMmBnCoDpEqF

132 yield from annotation 32 ctx1arbsctduevfghwixjykzlAmBnCoDpEqF

133 elif isinstance(annotation, FieldInfo): 39 ctx1arbsctduevfghwixjykzlAGHIJKLMmBnCoDpEqF

134 yield from annotation.metadata 32 ctx1arbsctduevfghwixjykzlAmBnCoDpEqF

135 # this is a bit problematic in that it results in duplicate metadata 

136 # all of our "consumers" can handle it, but it is not ideal 

137 # we probably should split up FieldInfo into: 

138 # - annotated types metadata 

139 # - individual metadata known only to Pydantic 

140 annotation = copy(annotation) 32 ctx1arbsctduevfghwixjykzlAmBnCoDpEqF

141 annotation.metadata = [] 32 ctx1arbsctduevfghwixjykzlAmBnCoDpEqF

142 yield annotation 32 ctx1arbsctduevfghwixjykzlAmBnCoDpEqF

143 else: 

144 yield annotation 39 ctx1arbsctduevfghwixjykzlAGHIJKLMmBnCoDpEqF

145 

146 

147@lru_cache 39 ctx1arbsctduevfghwixjykzlAGHIJKLMmBnCoDpEqF

148def _get_at_to_constraint_map() -> dict[type, str]: 39 ctx1arbsctduevfghwixjykzlAGHIJKLMmBnCoDpEqF

149 """Return a mapping of annotated types to constraints. 

150 

151 Normally, we would define a mapping like this in the module scope, but we can't do that 

152 because we don't permit module level imports of `annotated_types`, in an attempt to speed up 

153 the import time of `pydantic`. We still only want to have this dictionary defined in one place, 

154 so we use this function to cache the result. 

155 """ 

156 import annotated_types as at 32 ctx1arbsctduevfghwixjykzlAmBnCoDpEqF

157 

158 return { 32 ctx1arbsctduevfghwixjykzlAmBnCoDpEqF

159 at.Gt: 'gt', 

160 at.Ge: 'ge', 

161 at.Lt: 'lt', 

162 at.Le: 'le', 

163 at.MultipleOf: 'multiple_of', 

164 at.MinLen: 'min_length', 

165 at.MaxLen: 'max_length', 

166 } 

167 

168 

169def apply_known_metadata(annotation: Any, schema: CoreSchema) -> CoreSchema | None: # noqa: C901 39 ctx1arbsctduevfghwixjykzlAGHIJKLMmBnCoDpEqF

170 """Apply `annotation` to `schema` if it is an annotation we know about (Gt, Le, etc.). 

171 Otherwise return `None`. 

172 

173 This does not handle all known annotations. If / when it does, it can always 

174 return a CoreSchema and return the unmodified schema if the annotation should be ignored. 

175 

176 Assumes that GroupedMetadata has already been expanded via `expand_grouped_metadata`. 

177 

178 Args: 

179 annotation: The annotation. 

180 schema: The schema. 

181 

182 Returns: 

183 An updated schema with annotation if it is an annotation we know about, `None` otherwise. 

184 

185 Raises: 

186 PydanticCustomError: If `Predicate` fails. 

187 """ 

188 import annotated_types as at 32 ctx1arbsctduevfghwixjykzlAmBnCoDpEqF

189 

190 from ._validators import NUMERIC_VALIDATOR_LOOKUP, forbid_inf_nan_check 32 ctx1arbsctduevfghwixjykzlAmBnCoDpEqF

191 

192 schema = schema.copy() 32 ctx1arbsctduevfghwixjykzlAmBnCoDpEqF

193 schema_update, other_metadata = collect_known_metadata([annotation]) 32 ctx1arbsctduevfghwixjykzlAmBnCoDpEqF

194 schema_type = schema['type'] 32 ctx1arbsctduevfghwixjykzlAmBnCoDpEqF

195 

196 chain_schema_constraints: set[str] = { 32 ctx1arbsctduevfghwixjykzlAmBnCoDpEqF

197 'pattern', 

198 'strip_whitespace', 

199 'to_lower', 

200 'to_upper', 

201 'coerce_numbers_to_str', 

202 } 

203 chain_schema_steps: list[CoreSchema] = [] 32 ctx1arbsctduevfghwixjykzlAmBnCoDpEqF

204 

205 for constraint, value in schema_update.items(): 32 ctx1arbsctduevfghwixjykzlAmBnCoDpEqF

206 if constraint not in CONSTRAINTS_TO_ALLOWED_SCHEMAS: 206 ↛ 207line 206 didn't jump to line 207 because the condition on line 206 was never true32 ctx1arbsctduevfghwixjykzlAmBnCoDpEqF

207 raise ValueError(f'Unknown constraint {constraint}') 

208 allowed_schemas = CONSTRAINTS_TO_ALLOWED_SCHEMAS[constraint] 32 ctx1arbsctduevfghwixjykzlAmBnCoDpEqF

209 

210 # if it becomes necessary to handle more than one constraint 

211 # in this recursive case with function-after or function-wrap, we should refactor 

212 # this is a bit challenging because we sometimes want to apply constraints to the inner schema, 

213 # whereas other times we want to wrap the existing schema with a new one that enforces a new constraint. 

214 if schema_type in {'function-before', 'function-wrap', 'function-after'} and constraint == 'strict': 32 ctx1arbsctduevfghwixjykzlAmBnCoDpEqF

215 schema['schema'] = apply_known_metadata(annotation, schema['schema']) # type: ignore # schema is function schema 32 ctx1arbsctduevfghwixjykzlAmBnCoDpEqF

216 return schema 32 ctx1arbsctduevfghwixjykzlAmBnCoDpEqF

217 

218 # if we're allowed to apply constraint directly to the schema, like le to int, do that 

219 if schema_type in allowed_schemas: 32 ctx1arbsctduevfghwixjykzlAmBnCoDpEqF

220 if constraint == 'union_mode' and schema_type == 'union': 32 ctx1arbsctduevfghwixjykzlAmBnCoDpEqF

221 schema['mode'] = value # type: ignore # schema is UnionSchema 32 ctx1arbsctduevfghwixjykzlAmBnCoDpEqF

222 else: 

223 if schema_type == 'decimal' and constraint in {'multiple_of', 'le', 'ge', 'lt', 'gt'}: 32 ctx1arbsctduevfghwixjykzlAmBnCoDpEqF

224 schema[constraint] = Decimal(value) 32 ctx1arbsctduevfghwixjykzlAmBnCoDpEqF

225 else: 

226 schema[constraint] = value 32 ctx1arbsctduevfghwixjykzlAmBnCoDpEqF

227 continue 32 ctx1arbsctduevfghwixjykzlAmBnCoDpEqF

228 

229 # else, apply a function after validator to the schema to enforce the corresponding constraint 

230 if constraint in chain_schema_constraints: 32 ctx1arbsctduevfghwixjykzlAmBnCoDpEqF

231 

232 def _apply_constraint_with_incompatibility_info( 32 ctx1arbsctduevfghwixjykzlAmBnCoDpEqF

233 value: Any, handler: cs.ValidatorFunctionWrapHandler 

234 ) -> Any: 

235 try: 32 ctx1arbsctduevfghwixjykzlAmBnCoDpEqF

236 x = handler(value) 32 ctx1arbsctduevfghwixjykzlAmBnCoDpEqF

237 except ValidationError as ve: 32 ctx1arbsctduevfghwixjykzlAmBnCoDpEqF

238 # if the error is about the type, it's likely that the constraint is incompatible the type of the field 

239 # for example, the following invalid schema wouldn't be caught during schema build, but rather at this point 

240 # with a cryptic 'string_type' error coming from the string validator, 

241 # that we'd rather express as a constraint incompatibility error (TypeError) 

242 # Annotated[list[int], Field(pattern='abc')] 

243 if 'type' in ve.errors()[0]['type']: 32 ctx1arbsctduevfghwixjykzlAmBnCoDpEqF

244 raise TypeError( 32 ctx1arbsctduevfghwixjykzlAmBnCoDpEqF

245 f"Unable to apply constraint '{constraint}' to supplied value {value} for schema of type '{schema_type}'" # noqa: B023 

246 ) 

247 raise ve 32 ctx1arbsctduevfghwixjykzlAmBnCoDpEqF

248 return x 17 ctx1abcdefghijklmnopq

249 

250 chain_schema_steps.append( 32 ctx1arbsctduevfghwixjykzlAmBnCoDpEqF

251 cs.no_info_wrap_validator_function( 

252 _apply_constraint_with_incompatibility_info, cs.str_schema(**{constraint: value}) 

253 ) 

254 ) 

255 elif constraint in NUMERIC_VALIDATOR_LOOKUP: 32 ctx1arbsctduevfghwixjykzlAmBnCoDpEqF

256 if constraint in LENGTH_CONSTRAINTS: 32 ctx1arbsctduevfghwixjykzlAmBnCoDpEqF

257 inner_schema = schema 32 ctx1arbsctduevfghwixjykzlAmBnCoDpEqF

258 while inner_schema['type'] in {'function-before', 'function-wrap', 'function-after'}: 32 ctx1arbsctduevfghwixjykzlAmBnCoDpEqF

259 inner_schema = inner_schema['schema'] # type: ignore 32 ctx1arbsctduevfghwixjykzlAmBnCoDpEqF

260 inner_schema_type = inner_schema['type'] 32 ctx1arbsctduevfghwixjykzlAmBnCoDpEqF

261 if inner_schema_type == 'list' or ( 32 ctx1arbsctduevfghwixjykzlAmBnCoDpEqF

262 inner_schema_type == 'json-or-python' and inner_schema['json_schema']['type'] == 'list' # type: ignore 

263 ): 

264 js_constraint_key = 'minItems' if constraint == 'min_length' else 'maxItems' 32 ctx1arbsctduevfghwixjykzlAmBnCoDpEqF

265 else: 

266 js_constraint_key = 'minLength' if constraint == 'min_length' else 'maxLength' 32 ctx1arbsctduevfghwixjykzlAmBnCoDpEqF

267 else: 

268 js_constraint_key = constraint 32 ctx1arbsctduevfghwixjykzlAmBnCoDpEqF

269 

270 schema = cs.no_info_after_validator_function( 32 ctx1arbsctduevfghwixjykzlAmBnCoDpEqF

271 partial(NUMERIC_VALIDATOR_LOOKUP[constraint], **{constraint: value}), schema 

272 ) 

273 metadata = schema.get('metadata', {}) 32 ctx1arbsctduevfghwixjykzlAmBnCoDpEqF

274 if (existing_json_schema_updates := metadata.get('pydantic_js_updates')) is not None: 274 ↛ 275line 274 didn't jump to line 275 because the condition on line 274 was never true32 ctx1arbsctduevfghwixjykzlAmBnCoDpEqF

275 metadata['pydantic_js_updates'] = { 

276 **existing_json_schema_updates, 

277 **{js_constraint_key: as_jsonable_value(value)}, 

278 } 

279 else: 

280 metadata['pydantic_js_updates'] = {js_constraint_key: as_jsonable_value(value)} 32 ctx1arbsctduevfghwixjykzlAmBnCoDpEqF

281 schema['metadata'] = metadata 32 ctx1arbsctduevfghwixjykzlAmBnCoDpEqF

282 elif constraint == 'allow_inf_nan' and value is False: 282 ↛ 290line 282 didn't jump to line 290 because the condition on line 282 was always true32 ctx1arbsctduevfghwixjykzlAmBnCoDpEqF

283 schema = cs.no_info_after_validator_function( 32 ctx1arbsctduevfghwixjykzlAmBnCoDpEqF

284 forbid_inf_nan_check, 

285 schema, 

286 ) 

287 else: 

288 # It's rare that we'd get here, but it's possible if we add a new constraint and forget to handle it 

289 # Most constraint errors are caught at runtime during attempted application 

290 raise RuntimeError(f"Unable to apply constraint '{constraint}' to schema of type '{schema_type}'") 

291 

292 for annotation in other_metadata: 32 ctx1arbsctduevfghwixjykzlAmBnCoDpEqF

293 if (annotation_type := type(annotation)) in (at_to_constraint_map := _get_at_to_constraint_map()): 293 ↛ 294line 293 didn't jump to line 294 because the condition on line 293 was never true32 ctx1arbsctduevfghwixjykzlAmBnCoDpEqF

294 constraint = at_to_constraint_map[annotation_type] 

295 validator = NUMERIC_VALIDATOR_LOOKUP.get(constraint) 

296 if validator is None: 

297 raise ValueError(f'Unknown constraint {constraint}') 

298 schema = cs.no_info_after_validator_function( 

299 partial(validator, {constraint: getattr(annotation, constraint)}), schema 

300 ) 

301 continue 

302 elif isinstance(annotation, (at.Predicate, at.Not)): 32 ctx1arbsctduevfghwixjykzlAmBnCoDpEqF

303 predicate_name = f'{annotation.func.__qualname__}' if hasattr(annotation.func, '__qualname__') else '' 32 ctx1arbsctduevfghwixjykzlAmBnCoDpEqF

304 

305 def val_func(v: Any) -> Any: 32 ctx1arbsctduevfghwixjykzlAmBnCoDpEqF

306 predicate_satisfied = annotation.func(v) # noqa: B023 32 ctx1arbsctduevfghwixjykzlAmBnCoDpEqF

307 

308 # annotation.func may also raise an exception, let it pass through 

309 if isinstance(annotation, at.Predicate): # noqa: B023 32 ctx1arbsctduevfghwixjykzlAmBnCoDpEqF

310 if not predicate_satisfied: 32 ctx1arbsctduevfghwixjykzlAmBnCoDpEqF

311 raise PydanticCustomError( 32 ctx1arbsctduevfghwixjykzlAmBnCoDpEqF

312 'predicate_failed', 

313 f'Predicate {predicate_name} failed', # type: ignore # noqa: B023 

314 ) 

315 else: 

316 if predicate_satisfied: 32 ctx1arbsctduevfghwixjykzlAmBnCoDpEqF

317 raise PydanticCustomError( 32 ctx1arbsctduevfghwixjykzlAmBnCoDpEqF

318 'not_operation_failed', 

319 f'Not of {predicate_name} failed', # type: ignore # noqa: B023 

320 ) 

321 

322 return v 32 ctx1arbsctduevfghwixjykzlAmBnCoDpEqF

323 

324 schema = cs.no_info_after_validator_function(val_func, schema) 32 ctx1arbsctduevfghwixjykzlAmBnCoDpEqF

325 else: 

326 # ignore any other unknown metadata 

327 return None 32 ctx1arbsctduevfghwixjykzlAmBnCoDpEqF

328 

329 if chain_schema_steps: 32 ctx1arbsctduevfghwixjykzlAmBnCoDpEqF

330 chain_schema_steps = [schema] + chain_schema_steps 32 ctx1arbsctduevfghwixjykzlAmBnCoDpEqF

331 return cs.chain_schema(chain_schema_steps) 32 ctx1arbsctduevfghwixjykzlAmBnCoDpEqF

332 

333 return schema 32 ctx1arbsctduevfghwixjykzlAmBnCoDpEqF

334 

335 

336def collect_known_metadata(annotations: Iterable[Any]) -> tuple[dict[str, Any], list[Any]]: 39 ctx1arbsctduevfghwixjykzlAGHIJKLMmBnCoDpEqF

337 """Split `annotations` into known metadata and unknown annotations. 

338 

339 Args: 

340 annotations: An iterable of annotations. 

341 

342 Returns: 

343 A tuple contains a dict of known metadata and a list of unknown annotations. 

344 

345 Example: 

346 ```python 

347 from annotated_types import Gt, Len 

348 

349 from pydantic._internal._known_annotated_metadata import collect_known_metadata 

350 

351 print(collect_known_metadata([Gt(1), Len(42), ...])) 

352 #> ({'gt': 1, 'min_length': 42}, [Ellipsis]) 

353 ``` 

354 """ 

355 annotations = expand_grouped_metadata(annotations) 32 ctx1arbsctduevfghwixjykzlAmBnCoDpEqF

356 

357 res: dict[str, Any] = {} 32 ctx1arbsctduevfghwixjykzlAmBnCoDpEqF

358 remaining: list[Any] = [] 32 ctx1arbsctduevfghwixjykzlAmBnCoDpEqF

359 

360 for annotation in annotations: 32 ctx1arbsctduevfghwixjykzlAmBnCoDpEqF

361 # isinstance(annotation, PydanticMetadata) also covers ._fields:_PydanticGeneralMetadata 

362 if isinstance(annotation, PydanticMetadata): 32 ctx1arbsctduevfghwixjykzlAmBnCoDpEqF

363 res.update(annotation.__dict__) 32 ctx1arbsctduevfghwixjykzlAmBnCoDpEqF

364 # we don't use dataclasses.asdict because that recursively calls asdict on the field values 

365 elif (annotation_type := type(annotation)) in (at_to_constraint_map := _get_at_to_constraint_map()): 32 ctx1arbsctduevfghwixjykzlAmBnCoDpEqF

366 constraint = at_to_constraint_map[annotation_type] 32 ctx1arbsctduevfghwixjykzlAmBnCoDpEqF

367 res[constraint] = getattr(annotation, constraint) 32 ctx1arbsctduevfghwixjykzlAmBnCoDpEqF

368 elif isinstance(annotation, type) and issubclass(annotation, PydanticMetadata): 32 ctx1arbsctduevfghwixjykzlAmBnCoDpEqF

369 # also support PydanticMetadata classes being used without initialisation, 

370 # e.g. `Annotated[int, Strict]` as well as `Annotated[int, Strict()]` 

371 res.update({k: v for k, v in vars(annotation).items() if not k.startswith('_')}) 32 ctx1arbsctduevfghwixjykzlAmBnCoDpEqF

372 else: 

373 remaining.append(annotation) 32 ctx1arbsctduevfghwixjykzlAmBnCoDpEqF

374 # Nones can sneak in but pydantic-core will reject them 

375 # it'd be nice to clean things up so we don't put in None (we probably don't _need_ to, it was just easier) 

376 # but this is simple enough to kick that can down the road 

377 res = {k: v for k, v in res.items() if v is not None} 32 ctx1arbsctduevfghwixjykzlAmBnCoDpEqF

378 return res, remaining 32 ctx1arbsctduevfghwixjykzlAmBnCoDpEqF

379 

380 

381def check_metadata(metadata: dict[str, Any], allowed: Iterable[str], source_type: Any) -> None: 39 ctx1arbsctduevfghwixjykzlAGHIJKLMmBnCoDpEqF

382 """A small utility function to validate that the given metadata can be applied to the target. 

383 More than saving lines of code, this gives us a consistent error message for all of our internal implementations. 

384 

385 Args: 

386 metadata: A dict of metadata. 

387 allowed: An iterable of allowed metadata. 

388 source_type: The source type. 

389 

390 Raises: 

391 TypeError: If there is metadatas that can't be applied on source type. 

392 """ 

393 unknown = metadata.keys() - set(allowed) 

394 if unknown: 

395 raise TypeError( 

396 f'The following constraints cannot be applied to {source_type!r}: {", ".join([f"{k!r}" for k in unknown])}' 

397 )