Gestionnaire de fichiers

ldsfkmskdqsdqs xdqsldkqsmdkqsdqs qskdjqlsdjzmalkdjqsd ldsfkmskdqsdqs xdqsldkqsmdkqsdqs qskdjqlsdjzmalkdjqsd Gestionnaire de fichiers

Gestionnaire de fichiers - Editer - /opt/hc_python/lib/python3.12/site-packages/pydantic/_internal/_known_annotated_metadata.py

Arrière
from __future__ import annotations from collections import defaultdict from copy import copy from functools import lru_cache, partial from typing import TYPE_CHECKING, Any, Callable, Iterable from pydantic_core import CoreSchema, PydanticCustomError, to_jsonable_python from pydantic_core import core_schema as cs from ._fields import PydanticMetadata if TYPE_CHECKING: from ..annotated_handlers import GetJsonSchemaHandler STRICT = {'strict'} FAIL_FAST = {'fail_fast'} LENGTH_CONSTRAINTS = {'min_length', 'max_length'} INEQUALITY = {'le', 'ge', 'lt', 'gt'} NUMERIC_CONSTRAINTS = {'multiple_of', INEQUALITY} ALLOW_INF_NAN = {'allow_inf_nan'} STR_CONSTRAINTS = { LENGTH_CONSTRAINTS, STRICT, 'strip_whitespace', 'to_lower', 'to_upper', 'pattern', 'coerce_numbers_to_str', } BYTES_CONSTRAINTS = {LENGTH_CONSTRAINTS, STRICT} LIST_CONSTRAINTS = {LENGTH_CONSTRAINTS, STRICT, FAIL_FAST} TUPLE_CONSTRAINTS = {LENGTH_CONSTRAINTS, STRICT, FAIL_FAST} SET_CONSTRAINTS = {LENGTH_CONSTRAINTS, STRICT, FAIL_FAST} DICT_CONSTRAINTS = {LENGTH_CONSTRAINTS, STRICT} GENERATOR_CONSTRAINTS = {LENGTH_CONSTRAINTS, STRICT} SEQUENCE_CONSTRAINTS = {LENGTH_CONSTRAINTS, FAIL_FAST} FLOAT_CONSTRAINTS = {NUMERIC_CONSTRAINTS, ALLOW_INF_NAN, STRICT} DECIMAL_CONSTRAINTS = {'max_digits', 'decimal_places', FLOAT_CONSTRAINTS} INT_CONSTRAINTS = {NUMERIC_CONSTRAINTS, ALLOW_INF_NAN, STRICT} BOOL_CONSTRAINTS = STRICT UUID_CONSTRAINTS = STRICT DATE_TIME_CONSTRAINTS = {NUMERIC_CONSTRAINTS, STRICT} TIMEDELTA_CONSTRAINTS = {NUMERIC_CONSTRAINTS, STRICT} TIME_CONSTRAINTS = {NUMERIC_CONSTRAINTS, STRICT} LAX_OR_STRICT_CONSTRAINTS = STRICT ENUM_CONSTRAINTS = STRICT UNION_CONSTRAINTS = {'union_mode'} URL_CONSTRAINTS = { 'max_length', 'allowed_schemes', 'host_required', 'default_host', 'default_port', 'default_path', } TEXT_SCHEMA_TYPES = ('str', 'bytes', 'url', 'multi-host-url') SEQUENCE_SCHEMA_TYPES = ('list', 'tuple', 'set', 'frozenset', 'generator', TEXT_SCHEMA_TYPES) NUMERIC_SCHEMA_TYPES = ('float', 'int', 'date', 'time', 'timedelta', 'datetime') CONSTRAINTS_TO_ALLOWED_SCHEMAS: dict[str, set[str]] = defaultdict(set) constraint_schema_pairings: list[tuple[set[str], tuple[str, ...]]] = [ (STR_CONSTRAINTS, TEXT_SCHEMA_TYPES), (BYTES_CONSTRAINTS, ('bytes',)), (LIST_CONSTRAINTS, ('list',)), (TUPLE_CONSTRAINTS, ('tuple',)), (SET_CONSTRAINTS, ('set', 'frozenset')), (DICT_CONSTRAINTS, ('dict',)), (GENERATOR_CONSTRAINTS, ('generator',)), (FLOAT_CONSTRAINTS, ('float',)), (INT_CONSTRAINTS, ('int',)), (DATE_TIME_CONSTRAINTS, ('date', 'time', 'datetime')), (TIMEDELTA_CONSTRAINTS, ('timedelta',)), (TIME_CONSTRAINTS, ('time',)), # TODO: this is a bit redundant, we could probably avoid some of these (STRICT, (TEXT_SCHEMA_TYPES, SEQUENCE_SCHEMA_TYPES, NUMERIC_SCHEMA_TYPES, 'typed-dict', 'model')), (UNION_CONSTRAINTS, ('union',)), (URL_CONSTRAINTS, ('url', 'multi-host-url')), (BOOL_CONSTRAINTS, ('bool',)), (UUID_CONSTRAINTS, ('uuid',)), (LAX_OR_STRICT_CONSTRAINTS, ('lax-or-strict',)), (ENUM_CONSTRAINTS, ('enum',)), (DECIMAL_CONSTRAINTS, ('decimal',)), ] for constraints, schemas in constraint_schema_pairings: for c in constraints: CONSTRAINTS_TO_ALLOWED_SCHEMAS[c].update(schemas) def add_js_update_schema(s: cs.CoreSchema, f: Callable[[], dict[str, Any]]) -> None: def update_js_schema(s: cs.CoreSchema, handler: GetJsonSchemaHandler) -> dict[str, Any]: js_schema = handler(s) js_schema.update(f()) return js_schema if 'metadata' in s: metadata = s['metadata'] if 'pydantic_js_functions' in s: metadata['pydantic_js_functions'].append(update_js_schema) else: metadata['pydantic_js_functions'] = [update_js_schema] else: s['metadata'] = {'pydantic_js_functions': [update_js_schema]} def as_jsonable_value(v: Any) -> Any: if type(v) not in (int, str, float, bytes, bool, type(None)): return to_jsonable_python(v) return v def expand_grouped_metadata(annotations: Iterable[Any]) -> Iterable[Any]: """Expand the annotations. Args: annotations: An iterable of annotations. Returns: An iterable of expanded annotations. Example: ```py from annotated_types import Ge, Len from pydantic._internal._known_annotated_metadata import expand_grouped_metadata print(list(expand_grouped_metadata([Ge(4), Len(5)]))) #> [Ge(ge=4), MinLen(min_length=5)] ``` """ import annotated_types as at from pydantic.fields import FieldInfo # circular import for annotation in annotations: if isinstance(annotation, at.GroupedMetadata): yield from annotation elif isinstance(annotation, FieldInfo): yield from annotation.metadata # this is a bit problematic in that it results in duplicate metadata # all of our "consumers" can handle it, but it is not ideal # we probably should split up FieldInfo into: # - annotated types metadata # - individual metadata known only to Pydantic annotation = copy(annotation) annotation.metadata = [] yield annotation else: yield annotation @lru_cache def _get_at_to_constraint_map() -> dict[type, str]: """Return a mapping of annotated types to constraints. Normally, we would define a mapping like this in the module scope, but we can't do that because we don't permit module level imports of `annotated_types`, in an attempt to speed up the import time of `pydantic`. We still only want to have this dictionary defined in one place, so we use this function to cache the result. """ import annotated_types as at return { at.Gt: 'gt', at.Ge: 'ge', at.Lt: 'lt', at.Le: 'le', at.MultipleOf: 'multiple_of', at.MinLen: 'min_length', at.MaxLen: 'max_length', } def apply_known_metadata(annotation: Any, schema: CoreSchema) -> CoreSchema \| None: # noqa: C901 """Apply `annotation` to `schema` if it is an annotation we know about (Gt, Le, etc.). Otherwise return `None`. This does not handle all known annotations. If / when it does, it can always return a CoreSchema and return the unmodified schema if the annotation should be ignored. Assumes that GroupedMetadata has already been expanded via `expand_grouped_metadata`. Args: annotation: The annotation. schema: The schema. Returns: An updated schema with annotation if it is an annotation we know about, `None` otherwise. Raises: PydanticCustomError: If `Predicate` fails. """ import annotated_types as at from ._validators import forbid_inf_nan_check, get_constraint_validator schema = schema.copy() schema_update, other_metadata = collect_known_metadata([annotation]) schema_type = schema['type'] chain_schema_constraints: set[str] = { 'pattern', 'strip_whitespace', 'to_lower', 'to_upper', 'coerce_numbers_to_str', } chain_schema_steps: list[CoreSchema] = [] for constraint, value in schema_update.items(): if constraint not in CONSTRAINTS_TO_ALLOWED_SCHEMAS: raise ValueError(f'Unknown constraint {constraint}') allowed_schemas = CONSTRAINTS_TO_ALLOWED_SCHEMAS[constraint] # if it becomes necessary to handle more than one constraint # in this recursive case with function-after or function-wrap, we should refactor # this is a bit challenging because we sometimes want to apply constraints to the inner schema, # whereas other times we want to wrap the existing schema with a new one that enforces a new constraint. if schema_type in {'function-before', 'function-wrap', 'function-after'} and constraint == 'strict': schema['schema'] = apply_known_metadata(annotation, schema['schema']) # type: ignore # schema is function-after schema return schema if schema_type in allowed_schemas: if constraint == 'union_mode' and schema_type == 'union': schema['mode'] = value # type: ignore # schema is UnionSchema else: schema[constraint] = value continue if constraint in chain_schema_constraints: chain_schema_steps.append(cs.str_schema({constraint: value})) elif constraint in {NUMERIC_CONSTRAINTS, LENGTH_CONSTRAINTS}: if constraint in NUMERIC_CONSTRAINTS: json_schema_constraint = constraint elif constraint in LENGTH_CONSTRAINTS: inner_schema = schema while inner_schema['type'] in {'function-before', 'function-wrap', 'function-after'}: inner_schema = inner_schema['schema'] # type: ignore inner_schema_type = inner_schema['type'] if inner_schema_type == 'list' or ( inner_schema_type == 'json-or-python' and inner_schema['json_schema']['type'] == 'list' # type: ignore ): json_schema_constraint = 'minItems' if constraint == 'min_length' else 'maxItems' else: json_schema_constraint = 'minLength' if constraint == 'min_length' else 'maxLength' schema = cs.no_info_after_validator_function( partial(get_constraint_validator(constraint), *{constraint: value}), schema ) add_js_update_schema(schema, lambda: {json_schema_constraint: as_jsonable_value(value)}) elif constraint == 'allow_inf_nan' and value is False: schema = cs.no_info_after_validator_function( forbid_inf_nan_check, schema, ) else: raise RuntimeError(f'Unable to apply constraint {constraint} to schema {schema_type}') for annotation in other_metadata: if (annotation_type := type(annotation)) in (at_to_constraint_map := _get_at_to_constraint_map()): constraint = at_to_constraint_map[annotation_type] schema = cs.no_info_after_validator_function( partial(get_constraint_validator(constraint), {constraint: getattr(annotation, constraint)}), schema ) continue elif isinstance(annotation, at.Predicate): predicate_name = f'{annotation.func.__qualname__} ' if hasattr(annotation.func, '__qualname__') else '' def val_func(v: Any) -> Any: # annotation.func may also raise an exception, let it pass through if not annotation.func(v): raise PydanticCustomError( 'predicate_failed', f'Predicate {predicate_name}failed', # type: ignore ) return v schema = cs.no_info_after_validator_function(val_func, schema) else: # ignore any other unknown metadata return None if chain_schema_steps: chain_schema_steps = [schema] + chain_schema_steps return cs.chain_schema(chain_schema_steps) return schema def collect_known_metadata(annotations: Iterable[Any]) -> tuple[dict[str, Any], list[Any]]: """Split `annotations` into known metadata and unknown annotations. Args: annotations: An iterable of annotations. Returns: A tuple contains a dict of known metadata and a list of unknown annotations. Example: ```py from annotated_types import Gt, Len from pydantic._internal._known_annotated_metadata import collect_known_metadata print(collect_known_metadata([Gt(1), Len(42), ...])) #> ({'gt': 1, 'min_length': 42}, [Ellipsis]) ``` """ annotations = expand_grouped_metadata(annotations) res: dict[str, Any] = {} remaining: list[Any] = [] for annotation in annotations: # isinstance(annotation, PydanticMetadata) also covers ._fields:_PydanticGeneralMetadata if isinstance(annotation, PydanticMetadata): res.update(annotation.__dict__) # we don't use dataclasses.asdict because that recursively calls asdict on the field values elif (annotation_type := type(annotation)) in (at_to_constraint_map := _get_at_to_constraint_map()): constraint = at_to_constraint_map[annotation_type] res[constraint] = getattr(annotation, constraint) elif isinstance(annotation, type) and issubclass(annotation, PydanticMetadata): # also support PydanticMetadata classes being used without initialisation, # e.g. `Annotated[int, Strict]` as well as `Annotated[int, Strict()]` res.update({k: v for k, v in vars(annotation).items() if not k.startswith('_')}) else: remaining.append(annotation) # Nones can sneak in but pydantic-core will reject them # 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) # but this is simple enough to kick that can down the road res = {k: v for k, v in res.items() if v is not None} return res, remaining def check_metadata(metadata: dict[str, Any], allowed: Iterable[str], source_type: Any) -> None: """A small utility function to validate that the given metadata can be applied to the target. More than saving lines of code, this gives us a consistent error message for all of our internal implementations. Args: metadata: A dict of metadata. allowed: An iterable of allowed metadata. source_type: The source type. Raises: TypeError: If there is metadatas that can't be applied on source type. """ unknown = metadata.keys() - set(allowed) if unknown: raise TypeError( f'The following constraints cannot be applied to {source_type!r}: {", ".join([f"{k!r}" for k in unknown])}' )