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1154 lines
46 KiB
1154 lines
46 KiB
import re
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import warnings
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from collections import defaultdict
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from datetime import date, datetime, time, timedelta
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from decimal import Decimal
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from enum import Enum
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from ipaddress import IPv4Address, IPv4Interface, IPv4Network, IPv6Address, IPv6Interface, IPv6Network
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from pathlib import Path
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from typing import (
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TYPE_CHECKING,
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Any,
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Callable,
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Dict,
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ForwardRef,
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FrozenSet,
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Generic,
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Iterable,
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List,
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Optional,
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Pattern,
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Sequence,
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Set,
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Tuple,
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Type,
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TypeVar,
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Union,
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cast,
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)
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from uuid import UUID
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from typing_extensions import Annotated, Literal
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from .fields import (
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MAPPING_LIKE_SHAPES,
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SHAPE_DEQUE,
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SHAPE_FROZENSET,
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SHAPE_GENERIC,
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SHAPE_ITERABLE,
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SHAPE_LIST,
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SHAPE_SEQUENCE,
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SHAPE_SET,
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SHAPE_SINGLETON,
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SHAPE_TUPLE,
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SHAPE_TUPLE_ELLIPSIS,
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FieldInfo,
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ModelField,
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)
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from .json import pydantic_encoder
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from .networks import AnyUrl, EmailStr
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from .types import (
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ConstrainedDecimal,
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ConstrainedFloat,
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ConstrainedFrozenSet,
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ConstrainedInt,
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ConstrainedList,
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ConstrainedSet,
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SecretBytes,
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SecretStr,
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StrictBytes,
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StrictStr,
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conbytes,
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condecimal,
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confloat,
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confrozenset,
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conint,
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conlist,
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conset,
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constr,
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)
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from .typing import (
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all_literal_values,
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get_args,
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get_origin,
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get_sub_types,
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is_callable_type,
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is_literal_type,
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is_namedtuple,
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is_none_type,
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is_union,
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)
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from .utils import ROOT_KEY, get_model, lenient_issubclass
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if TYPE_CHECKING:
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from .dataclasses import Dataclass
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from .main import BaseModel
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default_prefix = '#/definitions/'
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default_ref_template = '#/definitions/{model}'
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TypeModelOrEnum = Union[Type['BaseModel'], Type[Enum]]
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TypeModelSet = Set[TypeModelOrEnum]
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def _apply_modify_schema(
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modify_schema: Callable[..., None], field: Optional[ModelField], field_schema: Dict[str, Any]
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) -> None:
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from inspect import signature
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sig = signature(modify_schema)
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args = set(sig.parameters.keys())
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if 'field' in args or 'kwargs' in args:
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modify_schema(field_schema, field=field)
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else:
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modify_schema(field_schema)
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def schema(
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models: Sequence[Union[Type['BaseModel'], Type['Dataclass']]],
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*,
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by_alias: bool = True,
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title: Optional[str] = None,
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description: Optional[str] = None,
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ref_prefix: Optional[str] = None,
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ref_template: str = default_ref_template,
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) -> Dict[str, Any]:
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"""
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Process a list of models and generate a single JSON Schema with all of them defined in the ``definitions``
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top-level JSON key, including their sub-models.
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:param models: a list of models to include in the generated JSON Schema
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:param by_alias: generate the schemas using the aliases defined, if any
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:param title: title for the generated schema that includes the definitions
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:param description: description for the generated schema
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:param ref_prefix: the JSON Pointer prefix for schema references with ``$ref``, if None, will be set to the
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default of ``#/definitions/``. Update it if you want the schemas to reference the definitions somewhere
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else, e.g. for OpenAPI use ``#/components/schemas/``. The resulting generated schemas will still be at the
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top-level key ``definitions``, so you can extract them from there. But all the references will have the set
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prefix.
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:param ref_template: Use a ``string.format()`` template for ``$ref`` instead of a prefix. This can be useful
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for references that cannot be represented by ``ref_prefix`` such as a definition stored in another file. For
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a sibling json file in a ``/schemas`` directory use ``"/schemas/${model}.json#"``.
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:return: dict with the JSON Schema with a ``definitions`` top-level key including the schema definitions for
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the models and sub-models passed in ``models``.
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"""
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clean_models = [get_model(model) for model in models]
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flat_models = get_flat_models_from_models(clean_models)
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model_name_map = get_model_name_map(flat_models)
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definitions = {}
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output_schema: Dict[str, Any] = {}
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if title:
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output_schema['title'] = title
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if description:
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output_schema['description'] = description
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for model in clean_models:
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m_schema, m_definitions, m_nested_models = model_process_schema(
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model,
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by_alias=by_alias,
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model_name_map=model_name_map,
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ref_prefix=ref_prefix,
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ref_template=ref_template,
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)
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definitions.update(m_definitions)
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model_name = model_name_map[model]
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definitions[model_name] = m_schema
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if definitions:
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output_schema['definitions'] = definitions
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return output_schema
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def model_schema(
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model: Union[Type['BaseModel'], Type['Dataclass']],
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by_alias: bool = True,
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ref_prefix: Optional[str] = None,
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ref_template: str = default_ref_template,
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) -> Dict[str, Any]:
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"""
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Generate a JSON Schema for one model. With all the sub-models defined in the ``definitions`` top-level
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JSON key.
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:param model: a Pydantic model (a class that inherits from BaseModel)
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:param by_alias: generate the schemas using the aliases defined, if any
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:param ref_prefix: the JSON Pointer prefix for schema references with ``$ref``, if None, will be set to the
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default of ``#/definitions/``. Update it if you want the schemas to reference the definitions somewhere
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else, e.g. for OpenAPI use ``#/components/schemas/``. The resulting generated schemas will still be at the
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top-level key ``definitions``, so you can extract them from there. But all the references will have the set
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prefix.
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:param ref_template: Use a ``string.format()`` template for ``$ref`` instead of a prefix. This can be useful for
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references that cannot be represented by ``ref_prefix`` such as a definition stored in another file. For a
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sibling json file in a ``/schemas`` directory use ``"/schemas/${model}.json#"``.
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:return: dict with the JSON Schema for the passed ``model``
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"""
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model = get_model(model)
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flat_models = get_flat_models_from_model(model)
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model_name_map = get_model_name_map(flat_models)
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model_name = model_name_map[model]
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m_schema, m_definitions, nested_models = model_process_schema(
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model, by_alias=by_alias, model_name_map=model_name_map, ref_prefix=ref_prefix, ref_template=ref_template
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)
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if model_name in nested_models:
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# model_name is in Nested models, it has circular references
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m_definitions[model_name] = m_schema
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m_schema = get_schema_ref(model_name, ref_prefix, ref_template, False)
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if m_definitions:
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m_schema.update({'definitions': m_definitions})
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return m_schema
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def get_field_info_schema(field: ModelField, schema_overrides: bool = False) -> Tuple[Dict[str, Any], bool]:
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# If no title is explicitly set, we don't set title in the schema for enums.
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# The behaviour is the same as `BaseModel` reference, where the default title
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# is in the definitions part of the schema.
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schema_: Dict[str, Any] = {}
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if field.field_info.title or not lenient_issubclass(field.type_, Enum):
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schema_['title'] = field.field_info.title or field.alias.title().replace('_', ' ')
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if field.field_info.title:
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schema_overrides = True
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if field.field_info.description:
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schema_['description'] = field.field_info.description
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schema_overrides = True
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if not field.required and field.default is not None and not is_callable_type(field.outer_type_):
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schema_['default'] = encode_default(field.default)
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schema_overrides = True
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return schema_, schema_overrides
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def field_schema(
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field: ModelField,
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*,
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by_alias: bool = True,
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model_name_map: Dict[TypeModelOrEnum, str],
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ref_prefix: Optional[str] = None,
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ref_template: str = default_ref_template,
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known_models: TypeModelSet = None,
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) -> Tuple[Dict[str, Any], Dict[str, Any], Set[str]]:
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"""
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Process a Pydantic field and return a tuple with a JSON Schema for it as the first item.
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Also return a dictionary of definitions with models as keys and their schemas as values. If the passed field
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is a model and has sub-models, and those sub-models don't have overrides (as ``title``, ``default``, etc), they
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will be included in the definitions and referenced in the schema instead of included recursively.
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:param field: a Pydantic ``ModelField``
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:param by_alias: use the defined alias (if any) in the returned schema
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:param model_name_map: used to generate the JSON Schema references to other models included in the definitions
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:param ref_prefix: the JSON Pointer prefix to use for references to other schemas, if None, the default of
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#/definitions/ will be used
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:param ref_template: Use a ``string.format()`` template for ``$ref`` instead of a prefix. This can be useful for
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references that cannot be represented by ``ref_prefix`` such as a definition stored in another file. For a
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sibling json file in a ``/schemas`` directory use ``"/schemas/${model}.json#"``.
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:param known_models: used to solve circular references
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:return: tuple of the schema for this field and additional definitions
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"""
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s, schema_overrides = get_field_info_schema(field)
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validation_schema = get_field_schema_validations(field)
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if validation_schema:
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s.update(validation_schema)
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schema_overrides = True
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f_schema, f_definitions, f_nested_models = field_type_schema(
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field,
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by_alias=by_alias,
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model_name_map=model_name_map,
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schema_overrides=schema_overrides,
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ref_prefix=ref_prefix,
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ref_template=ref_template,
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known_models=known_models or set(),
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)
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# $ref will only be returned when there are no schema_overrides
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if '$ref' in f_schema:
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return f_schema, f_definitions, f_nested_models
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else:
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s.update(f_schema)
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return s, f_definitions, f_nested_models
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numeric_types = (int, float, Decimal)
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_str_types_attrs: Tuple[Tuple[str, Union[type, Tuple[type, ...]], str], ...] = (
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('max_length', numeric_types, 'maxLength'),
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('min_length', numeric_types, 'minLength'),
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('regex', str, 'pattern'),
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)
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_numeric_types_attrs: Tuple[Tuple[str, Union[type, Tuple[type, ...]], str], ...] = (
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('gt', numeric_types, 'exclusiveMinimum'),
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('lt', numeric_types, 'exclusiveMaximum'),
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('ge', numeric_types, 'minimum'),
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('le', numeric_types, 'maximum'),
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('multiple_of', numeric_types, 'multipleOf'),
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)
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def get_field_schema_validations(field: ModelField) -> Dict[str, Any]:
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"""
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Get the JSON Schema validation keywords for a ``field`` with an annotation of
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a Pydantic ``FieldInfo`` with validation arguments.
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"""
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f_schema: Dict[str, Any] = {}
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if lenient_issubclass(field.type_, Enum):
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# schema is already updated by `enum_process_schema`; just update with field extra
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if field.field_info.extra:
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f_schema.update(field.field_info.extra)
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return f_schema
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if lenient_issubclass(field.type_, (str, bytes)):
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for attr_name, t, keyword in _str_types_attrs:
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attr = getattr(field.field_info, attr_name, None)
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if isinstance(attr, t):
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f_schema[keyword] = attr
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if lenient_issubclass(field.type_, numeric_types) and not issubclass(field.type_, bool):
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for attr_name, t, keyword in _numeric_types_attrs:
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attr = getattr(field.field_info, attr_name, None)
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if isinstance(attr, t):
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f_schema[keyword] = attr
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if field.field_info is not None and field.field_info.const:
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f_schema['const'] = field.default
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if field.field_info.extra:
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f_schema.update(field.field_info.extra)
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modify_schema = getattr(field.outer_type_, '__modify_schema__', None)
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if modify_schema:
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_apply_modify_schema(modify_schema, field, f_schema)
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return f_schema
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def get_model_name_map(unique_models: TypeModelSet) -> Dict[TypeModelOrEnum, str]:
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"""
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Process a set of models and generate unique names for them to be used as keys in the JSON Schema
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definitions. By default the names are the same as the class name. But if two models in different Python
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modules have the same name (e.g. "users.Model" and "items.Model"), the generated names will be
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based on the Python module path for those conflicting models to prevent name collisions.
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:param unique_models: a Python set of models
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:return: dict mapping models to names
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"""
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name_model_map = {}
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conflicting_names: Set[str] = set()
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for model in unique_models:
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model_name = normalize_name(model.__name__)
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if model_name in conflicting_names:
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model_name = get_long_model_name(model)
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name_model_map[model_name] = model
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elif model_name in name_model_map:
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conflicting_names.add(model_name)
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conflicting_model = name_model_map.pop(model_name)
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name_model_map[get_long_model_name(conflicting_model)] = conflicting_model
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name_model_map[get_long_model_name(model)] = model
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else:
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name_model_map[model_name] = model
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return {v: k for k, v in name_model_map.items()}
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def get_flat_models_from_model(model: Type['BaseModel'], known_models: TypeModelSet = None) -> TypeModelSet:
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"""
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Take a single ``model`` and generate a set with itself and all the sub-models in the tree. I.e. if you pass
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model ``Foo`` (subclass of Pydantic ``BaseModel``) as ``model``, and it has a field of type ``Bar`` (also
|
|
subclass of ``BaseModel``) and that model ``Bar`` has a field of type ``Baz`` (also subclass of ``BaseModel``),
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the return value will be ``set([Foo, Bar, Baz])``.
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|
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:param model: a Pydantic ``BaseModel`` subclass
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:param known_models: used to solve circular references
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:return: a set with the initial model and all its sub-models
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"""
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known_models = known_models or set()
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flat_models: TypeModelSet = set()
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flat_models.add(model)
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known_models |= flat_models
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fields = cast(Sequence[ModelField], model.__fields__.values())
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flat_models |= get_flat_models_from_fields(fields, known_models=known_models)
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return flat_models
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|
|
|
|
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def get_flat_models_from_field(field: ModelField, known_models: TypeModelSet) -> TypeModelSet:
|
|
"""
|
|
Take a single Pydantic ``ModelField`` (from a model) that could have been declared as a sublcass of BaseModel
|
|
(so, it could be a submodel), and generate a set with its model and all the sub-models in the tree.
|
|
I.e. if you pass a field that was declared to be of type ``Foo`` (subclass of BaseModel) as ``field``, and that
|
|
model ``Foo`` has a field of type ``Bar`` (also subclass of ``BaseModel``) and that model ``Bar`` has a field of
|
|
type ``Baz`` (also subclass of ``BaseModel``), the return value will be ``set([Foo, Bar, Baz])``.
|
|
|
|
:param field: a Pydantic ``ModelField``
|
|
:param known_models: used to solve circular references
|
|
:return: a set with the model used in the declaration for this field, if any, and all its sub-models
|
|
"""
|
|
from .main import BaseModel
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|
|
|
flat_models: TypeModelSet = set()
|
|
|
|
field_type = field.type_
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|
if lenient_issubclass(getattr(field_type, '__pydantic_model__', None), BaseModel):
|
|
field_type = field_type.__pydantic_model__
|
|
|
|
if field.sub_fields and not lenient_issubclass(field_type, BaseModel):
|
|
flat_models |= get_flat_models_from_fields(field.sub_fields, known_models=known_models)
|
|
elif lenient_issubclass(field_type, BaseModel) and field_type not in known_models:
|
|
flat_models |= get_flat_models_from_model(field_type, known_models=known_models)
|
|
elif lenient_issubclass(field_type, Enum):
|
|
flat_models.add(field_type)
|
|
return flat_models
|
|
|
|
|
|
def get_flat_models_from_fields(fields: Sequence[ModelField], known_models: TypeModelSet) -> TypeModelSet:
|
|
"""
|
|
Take a list of Pydantic ``ModelField``s (from a model) that could have been declared as subclasses of ``BaseModel``
|
|
(so, any of them could be a submodel), and generate a set with their models and all the sub-models in the tree.
|
|
I.e. if you pass a the fields of a model ``Foo`` (subclass of ``BaseModel``) as ``fields``, and on of them has a
|
|
field of type ``Bar`` (also subclass of ``BaseModel``) and that model ``Bar`` has a field of type ``Baz`` (also
|
|
subclass of ``BaseModel``), the return value will be ``set([Foo, Bar, Baz])``.
|
|
|
|
:param fields: a list of Pydantic ``ModelField``s
|
|
:param known_models: used to solve circular references
|
|
:return: a set with any model declared in the fields, and all their sub-models
|
|
"""
|
|
flat_models: TypeModelSet = set()
|
|
for field in fields:
|
|
flat_models |= get_flat_models_from_field(field, known_models=known_models)
|
|
return flat_models
|
|
|
|
|
|
def get_flat_models_from_models(models: Sequence[Type['BaseModel']]) -> TypeModelSet:
|
|
"""
|
|
Take a list of ``models`` and generate a set with them and all their sub-models in their trees. I.e. if you pass
|
|
a list of two models, ``Foo`` and ``Bar``, both subclasses of Pydantic ``BaseModel`` as models, and ``Bar`` has
|
|
a field of type ``Baz`` (also subclass of ``BaseModel``), the return value will be ``set([Foo, Bar, Baz])``.
|
|
"""
|
|
flat_models: TypeModelSet = set()
|
|
for model in models:
|
|
flat_models |= get_flat_models_from_model(model)
|
|
return flat_models
|
|
|
|
|
|
def get_long_model_name(model: TypeModelOrEnum) -> str:
|
|
return f'{model.__module__}__{model.__qualname__}'.replace('.', '__')
|
|
|
|
|
|
def field_type_schema(
|
|
field: ModelField,
|
|
*,
|
|
by_alias: bool,
|
|
model_name_map: Dict[TypeModelOrEnum, str],
|
|
ref_template: str,
|
|
schema_overrides: bool = False,
|
|
ref_prefix: Optional[str] = None,
|
|
known_models: TypeModelSet,
|
|
) -> Tuple[Dict[str, Any], Dict[str, Any], Set[str]]:
|
|
"""
|
|
Used by ``field_schema()``, you probably should be using that function.
|
|
|
|
Take a single ``field`` and generate the schema for its type only, not including additional
|
|
information as title, etc. Also return additional schema definitions, from sub-models.
|
|
"""
|
|
from .main import BaseModel # noqa: F811
|
|
|
|
definitions = {}
|
|
nested_models: Set[str] = set()
|
|
f_schema: Dict[str, Any]
|
|
if field.shape in {
|
|
SHAPE_LIST,
|
|
SHAPE_TUPLE_ELLIPSIS,
|
|
SHAPE_SEQUENCE,
|
|
SHAPE_SET,
|
|
SHAPE_FROZENSET,
|
|
SHAPE_ITERABLE,
|
|
SHAPE_DEQUE,
|
|
}:
|
|
items_schema, f_definitions, f_nested_models = field_singleton_schema(
|
|
field,
|
|
by_alias=by_alias,
|
|
model_name_map=model_name_map,
|
|
ref_prefix=ref_prefix,
|
|
ref_template=ref_template,
|
|
known_models=known_models,
|
|
)
|
|
definitions.update(f_definitions)
|
|
nested_models.update(f_nested_models)
|
|
f_schema = {'type': 'array', 'items': items_schema}
|
|
if field.shape in {SHAPE_SET, SHAPE_FROZENSET}:
|
|
f_schema['uniqueItems'] = True
|
|
|
|
elif field.shape in MAPPING_LIKE_SHAPES:
|
|
f_schema = {'type': 'object'}
|
|
key_field = cast(ModelField, field.key_field)
|
|
regex = getattr(key_field.type_, 'regex', None)
|
|
items_schema, f_definitions, f_nested_models = field_singleton_schema(
|
|
field,
|
|
by_alias=by_alias,
|
|
model_name_map=model_name_map,
|
|
ref_prefix=ref_prefix,
|
|
ref_template=ref_template,
|
|
known_models=known_models,
|
|
)
|
|
definitions.update(f_definitions)
|
|
nested_models.update(f_nested_models)
|
|
if regex:
|
|
# Dict keys have a regex pattern
|
|
# items_schema might be a schema or empty dict, add it either way
|
|
f_schema['patternProperties'] = {regex.pattern: items_schema}
|
|
elif items_schema:
|
|
# The dict values are not simply Any, so they need a schema
|
|
f_schema['additionalProperties'] = items_schema
|
|
elif field.shape == SHAPE_TUPLE or (field.shape == SHAPE_GENERIC and not issubclass(field.type_, BaseModel)):
|
|
sub_schema = []
|
|
sub_fields = cast(List[ModelField], field.sub_fields)
|
|
for sf in sub_fields:
|
|
sf_schema, sf_definitions, sf_nested_models = field_type_schema(
|
|
sf,
|
|
by_alias=by_alias,
|
|
model_name_map=model_name_map,
|
|
ref_prefix=ref_prefix,
|
|
ref_template=ref_template,
|
|
known_models=known_models,
|
|
)
|
|
definitions.update(sf_definitions)
|
|
nested_models.update(sf_nested_models)
|
|
sub_schema.append(sf_schema)
|
|
|
|
sub_fields_len = len(sub_fields)
|
|
if field.shape == SHAPE_GENERIC:
|
|
all_of_schemas = sub_schema[0] if sub_fields_len == 1 else {'type': 'array', 'items': sub_schema}
|
|
f_schema = {'allOf': [all_of_schemas]}
|
|
else:
|
|
f_schema = {
|
|
'type': 'array',
|
|
'minItems': sub_fields_len,
|
|
'maxItems': sub_fields_len,
|
|
}
|
|
if sub_fields_len >= 1:
|
|
f_schema['items'] = sub_schema
|
|
else:
|
|
assert field.shape in {SHAPE_SINGLETON, SHAPE_GENERIC}, field.shape
|
|
f_schema, f_definitions, f_nested_models = field_singleton_schema(
|
|
field,
|
|
by_alias=by_alias,
|
|
model_name_map=model_name_map,
|
|
schema_overrides=schema_overrides,
|
|
ref_prefix=ref_prefix,
|
|
ref_template=ref_template,
|
|
known_models=known_models,
|
|
)
|
|
definitions.update(f_definitions)
|
|
nested_models.update(f_nested_models)
|
|
|
|
# check field type to avoid repeated calls to the same __modify_schema__ method
|
|
if field.type_ != field.outer_type_:
|
|
if field.shape == SHAPE_GENERIC:
|
|
field_type = field.type_
|
|
else:
|
|
field_type = field.outer_type_
|
|
modify_schema = getattr(field_type, '__modify_schema__', None)
|
|
if modify_schema:
|
|
_apply_modify_schema(modify_schema, field, f_schema)
|
|
return f_schema, definitions, nested_models
|
|
|
|
|
|
def model_process_schema(
|
|
model: TypeModelOrEnum,
|
|
*,
|
|
by_alias: bool = True,
|
|
model_name_map: Dict[TypeModelOrEnum, str],
|
|
ref_prefix: Optional[str] = None,
|
|
ref_template: str = default_ref_template,
|
|
known_models: TypeModelSet = None,
|
|
field: Optional[ModelField] = None,
|
|
) -> Tuple[Dict[str, Any], Dict[str, Any], Set[str]]:
|
|
"""
|
|
Used by ``model_schema()``, you probably should be using that function.
|
|
|
|
Take a single ``model`` and generate its schema. Also return additional schema definitions, from sub-models. The
|
|
sub-models of the returned schema will be referenced, but their definitions will not be included in the schema. All
|
|
the definitions are returned as the second value.
|
|
"""
|
|
from inspect import getdoc, signature
|
|
|
|
known_models = known_models or set()
|
|
if lenient_issubclass(model, Enum):
|
|
model = cast(Type[Enum], model)
|
|
s = enum_process_schema(model, field=field)
|
|
return s, {}, set()
|
|
model = cast(Type['BaseModel'], model)
|
|
s = {'title': model.__config__.title or model.__name__}
|
|
doc = getdoc(model)
|
|
if doc:
|
|
s['description'] = doc
|
|
known_models.add(model)
|
|
m_schema, m_definitions, nested_models = model_type_schema(
|
|
model,
|
|
by_alias=by_alias,
|
|
model_name_map=model_name_map,
|
|
ref_prefix=ref_prefix,
|
|
ref_template=ref_template,
|
|
known_models=known_models,
|
|
)
|
|
s.update(m_schema)
|
|
schema_extra = model.__config__.schema_extra
|
|
if callable(schema_extra):
|
|
if len(signature(schema_extra).parameters) == 1:
|
|
schema_extra(s)
|
|
else:
|
|
schema_extra(s, model)
|
|
else:
|
|
s.update(schema_extra)
|
|
return s, m_definitions, nested_models
|
|
|
|
|
|
def model_type_schema(
|
|
model: Type['BaseModel'],
|
|
*,
|
|
by_alias: bool,
|
|
model_name_map: Dict[TypeModelOrEnum, str],
|
|
ref_template: str,
|
|
ref_prefix: Optional[str] = None,
|
|
known_models: TypeModelSet,
|
|
) -> Tuple[Dict[str, Any], Dict[str, Any], Set[str]]:
|
|
"""
|
|
You probably should be using ``model_schema()``, this function is indirectly used by that function.
|
|
|
|
Take a single ``model`` and generate the schema for its type only, not including additional
|
|
information as title, etc. Also return additional schema definitions, from sub-models.
|
|
"""
|
|
properties = {}
|
|
required = []
|
|
definitions: Dict[str, Any] = {}
|
|
nested_models: Set[str] = set()
|
|
for k, f in model.__fields__.items():
|
|
try:
|
|
f_schema, f_definitions, f_nested_models = field_schema(
|
|
f,
|
|
by_alias=by_alias,
|
|
model_name_map=model_name_map,
|
|
ref_prefix=ref_prefix,
|
|
ref_template=ref_template,
|
|
known_models=known_models,
|
|
)
|
|
except SkipField as skip:
|
|
warnings.warn(skip.message, UserWarning)
|
|
continue
|
|
definitions.update(f_definitions)
|
|
nested_models.update(f_nested_models)
|
|
if by_alias:
|
|
properties[f.alias] = f_schema
|
|
if f.required:
|
|
required.append(f.alias)
|
|
else:
|
|
properties[k] = f_schema
|
|
if f.required:
|
|
required.append(k)
|
|
if ROOT_KEY in properties:
|
|
out_schema = properties[ROOT_KEY]
|
|
out_schema['title'] = model.__config__.title or model.__name__
|
|
else:
|
|
out_schema = {'type': 'object', 'properties': properties}
|
|
if required:
|
|
out_schema['required'] = required
|
|
if model.__config__.extra == 'forbid':
|
|
out_schema['additionalProperties'] = False
|
|
return out_schema, definitions, nested_models
|
|
|
|
|
|
def enum_process_schema(enum: Type[Enum], *, field: Optional[ModelField] = None) -> Dict[str, Any]:
|
|
"""
|
|
Take a single `enum` and generate its schema.
|
|
|
|
This is similar to the `model_process_schema` function, but applies to ``Enum`` objects.
|
|
"""
|
|
schema_: Dict[str, Any] = {
|
|
'title': enum.__name__,
|
|
# Python assigns all enums a default docstring value of 'An enumeration', so
|
|
# all enums will have a description field even if not explicitly provided.
|
|
'description': enum.__doc__ or 'An enumeration.',
|
|
# Add enum values and the enum field type to the schema.
|
|
'enum': [item.value for item in cast(Iterable[Enum], enum)],
|
|
}
|
|
|
|
add_field_type_to_schema(enum, schema_)
|
|
|
|
modify_schema = getattr(enum, '__modify_schema__', None)
|
|
if modify_schema:
|
|
_apply_modify_schema(modify_schema, field, schema_)
|
|
|
|
return schema_
|
|
|
|
|
|
def field_singleton_sub_fields_schema(
|
|
field: ModelField,
|
|
*,
|
|
by_alias: bool,
|
|
model_name_map: Dict[TypeModelOrEnum, str],
|
|
ref_template: str,
|
|
schema_overrides: bool = False,
|
|
ref_prefix: Optional[str] = None,
|
|
known_models: TypeModelSet,
|
|
) -> Tuple[Dict[str, Any], Dict[str, Any], Set[str]]:
|
|
"""
|
|
This function is indirectly used by ``field_schema()``, you probably should be using that function.
|
|
|
|
Take a list of Pydantic ``ModelField`` from the declaration of a type with parameters, and generate their
|
|
schema. I.e., fields used as "type parameters", like ``str`` and ``int`` in ``Tuple[str, int]``.
|
|
"""
|
|
sub_fields = cast(List[ModelField], field.sub_fields)
|
|
definitions = {}
|
|
nested_models: Set[str] = set()
|
|
if len(sub_fields) == 1:
|
|
return field_type_schema(
|
|
sub_fields[0],
|
|
by_alias=by_alias,
|
|
model_name_map=model_name_map,
|
|
schema_overrides=schema_overrides,
|
|
ref_prefix=ref_prefix,
|
|
ref_template=ref_template,
|
|
known_models=known_models,
|
|
)
|
|
else:
|
|
s: Dict[str, Any] = {}
|
|
# https://github.com/OAI/OpenAPI-Specification/blob/master/versions/3.0.2.md#discriminator-object
|
|
field_has_discriminator: bool = field.discriminator_key is not None
|
|
if field_has_discriminator:
|
|
assert field.sub_fields_mapping is not None
|
|
|
|
discriminator_models_refs: Dict[str, Union[str, Dict[str, Any]]] = {}
|
|
|
|
for discriminator_value, sub_field in field.sub_fields_mapping.items():
|
|
# sub_field is either a `BaseModel` or directly an `Annotated` `Union` of many
|
|
if is_union(get_origin(sub_field.type_)):
|
|
sub_models = get_sub_types(sub_field.type_)
|
|
discriminator_models_refs[discriminator_value] = {
|
|
model_name_map[sub_model]: get_schema_ref(
|
|
model_name_map[sub_model], ref_prefix, ref_template, False
|
|
)
|
|
for sub_model in sub_models
|
|
}
|
|
else:
|
|
sub_field_type = sub_field.type_
|
|
if hasattr(sub_field_type, '__pydantic_model__'):
|
|
sub_field_type = sub_field_type.__pydantic_model__
|
|
|
|
discriminator_model_name = model_name_map[sub_field_type]
|
|
discriminator_model_ref = get_schema_ref(discriminator_model_name, ref_prefix, ref_template, False)
|
|
discriminator_models_refs[discriminator_value] = discriminator_model_ref['$ref']
|
|
|
|
s['discriminator'] = {
|
|
'propertyName': field.discriminator_alias,
|
|
'mapping': discriminator_models_refs,
|
|
}
|
|
|
|
sub_field_schemas = []
|
|
for sf in sub_fields:
|
|
sub_schema, sub_definitions, sub_nested_models = field_type_schema(
|
|
sf,
|
|
by_alias=by_alias,
|
|
model_name_map=model_name_map,
|
|
schema_overrides=schema_overrides,
|
|
ref_prefix=ref_prefix,
|
|
ref_template=ref_template,
|
|
known_models=known_models,
|
|
)
|
|
definitions.update(sub_definitions)
|
|
if schema_overrides and 'allOf' in sub_schema:
|
|
# if the sub_field is a referenced schema we only need the referenced
|
|
# object. Otherwise we will end up with several allOf inside anyOf/oneOf.
|
|
# See https://github.com/pydantic/pydantic/issues/1209
|
|
sub_schema = sub_schema['allOf'][0]
|
|
|
|
if sub_schema.keys() == {'discriminator', 'oneOf'}:
|
|
# we don't want discriminator information inside oneOf choices, this is dealt with elsewhere
|
|
sub_schema.pop('discriminator')
|
|
sub_field_schemas.append(sub_schema)
|
|
nested_models.update(sub_nested_models)
|
|
s['oneOf' if field_has_discriminator else 'anyOf'] = sub_field_schemas
|
|
return s, definitions, nested_models
|
|
|
|
|
|
# Order is important, e.g. subclasses of str must go before str
|
|
# this is used only for standard library types, custom types should use __modify_schema__ instead
|
|
field_class_to_schema: Tuple[Tuple[Any, Dict[str, Any]], ...] = (
|
|
(Path, {'type': 'string', 'format': 'path'}),
|
|
(datetime, {'type': 'string', 'format': 'date-time'}),
|
|
(date, {'type': 'string', 'format': 'date'}),
|
|
(time, {'type': 'string', 'format': 'time'}),
|
|
(timedelta, {'type': 'number', 'format': 'time-delta'}),
|
|
(IPv4Network, {'type': 'string', 'format': 'ipv4network'}),
|
|
(IPv6Network, {'type': 'string', 'format': 'ipv6network'}),
|
|
(IPv4Interface, {'type': 'string', 'format': 'ipv4interface'}),
|
|
(IPv6Interface, {'type': 'string', 'format': 'ipv6interface'}),
|
|
(IPv4Address, {'type': 'string', 'format': 'ipv4'}),
|
|
(IPv6Address, {'type': 'string', 'format': 'ipv6'}),
|
|
(Pattern, {'type': 'string', 'format': 'regex'}),
|
|
(str, {'type': 'string'}),
|
|
(bytes, {'type': 'string', 'format': 'binary'}),
|
|
(bool, {'type': 'boolean'}),
|
|
(int, {'type': 'integer'}),
|
|
(float, {'type': 'number'}),
|
|
(Decimal, {'type': 'number'}),
|
|
(UUID, {'type': 'string', 'format': 'uuid'}),
|
|
(dict, {'type': 'object'}),
|
|
(list, {'type': 'array', 'items': {}}),
|
|
(tuple, {'type': 'array', 'items': {}}),
|
|
(set, {'type': 'array', 'items': {}, 'uniqueItems': True}),
|
|
(frozenset, {'type': 'array', 'items': {}, 'uniqueItems': True}),
|
|
)
|
|
|
|
json_scheme = {'type': 'string', 'format': 'json-string'}
|
|
|
|
|
|
def add_field_type_to_schema(field_type: Any, schema_: Dict[str, Any]) -> None:
|
|
"""
|
|
Update the given `schema` with the type-specific metadata for the given `field_type`.
|
|
|
|
This function looks through `field_class_to_schema` for a class that matches the given `field_type`,
|
|
and then modifies the given `schema` with the information from that type.
|
|
"""
|
|
for type_, t_schema in field_class_to_schema:
|
|
# Fallback for `typing.Pattern` and `re.Pattern` as they are not a valid class
|
|
if lenient_issubclass(field_type, type_) or field_type is type_ is Pattern:
|
|
schema_.update(t_schema)
|
|
break
|
|
|
|
|
|
def get_schema_ref(name: str, ref_prefix: Optional[str], ref_template: str, schema_overrides: bool) -> Dict[str, Any]:
|
|
if ref_prefix:
|
|
schema_ref = {'$ref': ref_prefix + name}
|
|
else:
|
|
schema_ref = {'$ref': ref_template.format(model=name)}
|
|
return {'allOf': [schema_ref]} if schema_overrides else schema_ref
|
|
|
|
|
|
def field_singleton_schema( # noqa: C901 (ignore complexity)
|
|
field: ModelField,
|
|
*,
|
|
by_alias: bool,
|
|
model_name_map: Dict[TypeModelOrEnum, str],
|
|
ref_template: str,
|
|
schema_overrides: bool = False,
|
|
ref_prefix: Optional[str] = None,
|
|
known_models: TypeModelSet,
|
|
) -> Tuple[Dict[str, Any], Dict[str, Any], Set[str]]:
|
|
"""
|
|
This function is indirectly used by ``field_schema()``, you should probably be using that function.
|
|
|
|
Take a single Pydantic ``ModelField``, and return its schema and any additional definitions from sub-models.
|
|
"""
|
|
from .main import BaseModel
|
|
|
|
definitions: Dict[str, Any] = {}
|
|
nested_models: Set[str] = set()
|
|
field_type = field.type_
|
|
|
|
# Recurse into this field if it contains sub_fields and is NOT a
|
|
# BaseModel OR that BaseModel is a const
|
|
if field.sub_fields and (
|
|
(field.field_info and field.field_info.const) or not lenient_issubclass(field_type, BaseModel)
|
|
):
|
|
return field_singleton_sub_fields_schema(
|
|
field,
|
|
by_alias=by_alias,
|
|
model_name_map=model_name_map,
|
|
schema_overrides=schema_overrides,
|
|
ref_prefix=ref_prefix,
|
|
ref_template=ref_template,
|
|
known_models=known_models,
|
|
)
|
|
if field_type is Any or field_type is object or field_type.__class__ == TypeVar or get_origin(field_type) is type:
|
|
return {}, definitions, nested_models # no restrictions
|
|
if is_none_type(field_type):
|
|
return {'type': 'null'}, definitions, nested_models
|
|
if is_callable_type(field_type):
|
|
raise SkipField(f'Callable {field.name} was excluded from schema since JSON schema has no equivalent type.')
|
|
f_schema: Dict[str, Any] = {}
|
|
if field.field_info is not None and field.field_info.const:
|
|
f_schema['const'] = field.default
|
|
|
|
if is_literal_type(field_type):
|
|
values = all_literal_values(field_type)
|
|
|
|
if len({v.__class__ for v in values}) > 1:
|
|
return field_schema(
|
|
multitypes_literal_field_for_schema(values, field),
|
|
by_alias=by_alias,
|
|
model_name_map=model_name_map,
|
|
ref_prefix=ref_prefix,
|
|
ref_template=ref_template,
|
|
known_models=known_models,
|
|
)
|
|
|
|
# All values have the same type
|
|
field_type = values[0].__class__
|
|
f_schema['enum'] = list(values)
|
|
add_field_type_to_schema(field_type, f_schema)
|
|
elif lenient_issubclass(field_type, Enum):
|
|
enum_name = model_name_map[field_type]
|
|
f_schema, schema_overrides = get_field_info_schema(field, schema_overrides)
|
|
f_schema.update(get_schema_ref(enum_name, ref_prefix, ref_template, schema_overrides))
|
|
definitions[enum_name] = enum_process_schema(field_type, field=field)
|
|
elif is_namedtuple(field_type):
|
|
sub_schema, *_ = model_process_schema(
|
|
field_type.__pydantic_model__,
|
|
by_alias=by_alias,
|
|
model_name_map=model_name_map,
|
|
ref_prefix=ref_prefix,
|
|
ref_template=ref_template,
|
|
known_models=known_models,
|
|
field=field,
|
|
)
|
|
items_schemas = list(sub_schema['properties'].values())
|
|
f_schema.update(
|
|
{
|
|
'type': 'array',
|
|
'items': items_schemas,
|
|
'minItems': len(items_schemas),
|
|
'maxItems': len(items_schemas),
|
|
}
|
|
)
|
|
elif not hasattr(field_type, '__pydantic_model__'):
|
|
add_field_type_to_schema(field_type, f_schema)
|
|
|
|
modify_schema = getattr(field_type, '__modify_schema__', None)
|
|
if modify_schema:
|
|
_apply_modify_schema(modify_schema, field, f_schema)
|
|
|
|
if f_schema:
|
|
return f_schema, definitions, nested_models
|
|
|
|
# Handle dataclass-based models
|
|
if lenient_issubclass(getattr(field_type, '__pydantic_model__', None), BaseModel):
|
|
field_type = field_type.__pydantic_model__
|
|
|
|
if issubclass(field_type, BaseModel):
|
|
model_name = model_name_map[field_type]
|
|
if field_type not in known_models:
|
|
sub_schema, sub_definitions, sub_nested_models = model_process_schema(
|
|
field_type,
|
|
by_alias=by_alias,
|
|
model_name_map=model_name_map,
|
|
ref_prefix=ref_prefix,
|
|
ref_template=ref_template,
|
|
known_models=known_models,
|
|
field=field,
|
|
)
|
|
definitions.update(sub_definitions)
|
|
definitions[model_name] = sub_schema
|
|
nested_models.update(sub_nested_models)
|
|
else:
|
|
nested_models.add(model_name)
|
|
schema_ref = get_schema_ref(model_name, ref_prefix, ref_template, schema_overrides)
|
|
return schema_ref, definitions, nested_models
|
|
|
|
# For generics with no args
|
|
args = get_args(field_type)
|
|
if args is not None and not args and Generic in field_type.__bases__:
|
|
return f_schema, definitions, nested_models
|
|
|
|
raise ValueError(f'Value not declarable with JSON Schema, field: {field}')
|
|
|
|
|
|
def multitypes_literal_field_for_schema(values: Tuple[Any, ...], field: ModelField) -> ModelField:
|
|
"""
|
|
To support `Literal` with values of different types, we split it into multiple `Literal` with same type
|
|
e.g. `Literal['qwe', 'asd', 1, 2]` becomes `Union[Literal['qwe', 'asd'], Literal[1, 2]]`
|
|
"""
|
|
literal_distinct_types = defaultdict(list)
|
|
for v in values:
|
|
literal_distinct_types[v.__class__].append(v)
|
|
distinct_literals = (Literal[tuple(same_type_values)] for same_type_values in literal_distinct_types.values())
|
|
|
|
return ModelField(
|
|
name=field.name,
|
|
type_=Union[tuple(distinct_literals)], # type: ignore
|
|
class_validators=field.class_validators,
|
|
model_config=field.model_config,
|
|
default=field.default,
|
|
required=field.required,
|
|
alias=field.alias,
|
|
field_info=field.field_info,
|
|
)
|
|
|
|
|
|
def encode_default(dft: Any) -> Any:
|
|
if isinstance(dft, Enum):
|
|
return dft.value
|
|
elif isinstance(dft, (int, float, str)):
|
|
return dft
|
|
elif isinstance(dft, (list, tuple)):
|
|
t = dft.__class__
|
|
seq_args = (encode_default(v) for v in dft)
|
|
return t(*seq_args) if is_namedtuple(t) else t(seq_args)
|
|
elif isinstance(dft, dict):
|
|
return {encode_default(k): encode_default(v) for k, v in dft.items()}
|
|
elif dft is None:
|
|
return None
|
|
else:
|
|
return pydantic_encoder(dft)
|
|
|
|
|
|
_map_types_constraint: Dict[Any, Callable[..., type]] = {int: conint, float: confloat, Decimal: condecimal}
|
|
|
|
|
|
def get_annotation_from_field_info(
|
|
annotation: Any, field_info: FieldInfo, field_name: str, validate_assignment: bool = False
|
|
) -> Type[Any]:
|
|
"""
|
|
Get an annotation with validation implemented for numbers and strings based on the field_info.
|
|
:param annotation: an annotation from a field specification, as ``str``, ``ConstrainedStr``
|
|
:param field_info: an instance of FieldInfo, possibly with declarations for validations and JSON Schema
|
|
:param field_name: name of the field for use in error messages
|
|
:param validate_assignment: default False, flag for BaseModel Config value of validate_assignment
|
|
:return: the same ``annotation`` if unmodified or a new annotation with validation in place
|
|
"""
|
|
constraints = field_info.get_constraints()
|
|
used_constraints: Set[str] = set()
|
|
if constraints:
|
|
annotation, used_constraints = get_annotation_with_constraints(annotation, field_info)
|
|
if validate_assignment:
|
|
used_constraints.add('allow_mutation')
|
|
|
|
unused_constraints = constraints - used_constraints
|
|
if unused_constraints:
|
|
raise ValueError(
|
|
f'On field "{field_name}" the following field constraints are set but not enforced: '
|
|
f'{", ".join(unused_constraints)}. '
|
|
f'\nFor more details see https://pydantic-docs.helpmanual.io/usage/schema/#unenforced-field-constraints'
|
|
)
|
|
|
|
return annotation
|
|
|
|
|
|
def get_annotation_with_constraints(annotation: Any, field_info: FieldInfo) -> Tuple[Type[Any], Set[str]]: # noqa: C901
|
|
"""
|
|
Get an annotation with used constraints implemented for numbers and strings based on the field_info.
|
|
|
|
:param annotation: an annotation from a field specification, as ``str``, ``ConstrainedStr``
|
|
:param field_info: an instance of FieldInfo, possibly with declarations for validations and JSON Schema
|
|
:return: the same ``annotation`` if unmodified or a new annotation along with the used constraints.
|
|
"""
|
|
used_constraints: Set[str] = set()
|
|
|
|
def go(type_: Any) -> Type[Any]:
|
|
if (
|
|
is_literal_type(type_)
|
|
or isinstance(type_, ForwardRef)
|
|
or lenient_issubclass(type_, (ConstrainedList, ConstrainedSet, ConstrainedFrozenSet))
|
|
):
|
|
return type_
|
|
origin = get_origin(type_)
|
|
if origin is not None:
|
|
args: Tuple[Any, ...] = get_args(type_)
|
|
if any(isinstance(a, ForwardRef) for a in args):
|
|
# forward refs cause infinite recursion below
|
|
return type_
|
|
|
|
if origin is Annotated:
|
|
return go(args[0])
|
|
if is_union(origin):
|
|
return Union[tuple(go(a) for a in args)] # type: ignore
|
|
|
|
if issubclass(origin, List) and (
|
|
field_info.min_items is not None
|
|
or field_info.max_items is not None
|
|
or field_info.unique_items is not None
|
|
):
|
|
used_constraints.update({'min_items', 'max_items', 'unique_items'})
|
|
return conlist(
|
|
go(args[0]),
|
|
min_items=field_info.min_items,
|
|
max_items=field_info.max_items,
|
|
unique_items=field_info.unique_items,
|
|
)
|
|
|
|
if issubclass(origin, Set) and (field_info.min_items is not None or field_info.max_items is not None):
|
|
used_constraints.update({'min_items', 'max_items'})
|
|
return conset(go(args[0]), min_items=field_info.min_items, max_items=field_info.max_items)
|
|
|
|
if issubclass(origin, FrozenSet) and (field_info.min_items is not None or field_info.max_items is not None):
|
|
used_constraints.update({'min_items', 'max_items'})
|
|
return confrozenset(go(args[0]), min_items=field_info.min_items, max_items=field_info.max_items)
|
|
|
|
for t in (Tuple, List, Set, FrozenSet, Sequence):
|
|
if issubclass(origin, t): # type: ignore
|
|
return t[tuple(go(a) for a in args)] # type: ignore
|
|
|
|
if issubclass(origin, Dict):
|
|
return Dict[args[0], go(args[1])] # type: ignore
|
|
|
|
attrs: Optional[Tuple[str, ...]] = None
|
|
constraint_func: Optional[Callable[..., type]] = None
|
|
if isinstance(type_, type):
|
|
if issubclass(type_, (SecretStr, SecretBytes)):
|
|
attrs = ('max_length', 'min_length')
|
|
|
|
def constraint_func(**kw: Any) -> Type[Any]:
|
|
return type(type_.__name__, (type_,), kw)
|
|
|
|
elif issubclass(type_, str) and not issubclass(type_, (EmailStr, AnyUrl)):
|
|
attrs = ('max_length', 'min_length', 'regex')
|
|
if issubclass(type_, StrictStr):
|
|
|
|
def constraint_func(**kw: Any) -> Type[Any]:
|
|
return type(type_.__name__, (type_,), kw)
|
|
|
|
else:
|
|
constraint_func = constr
|
|
elif issubclass(type_, bytes):
|
|
attrs = ('max_length', 'min_length', 'regex')
|
|
if issubclass(type_, StrictBytes):
|
|
|
|
def constraint_func(**kw: Any) -> Type[Any]:
|
|
return type(type_.__name__, (type_,), kw)
|
|
|
|
else:
|
|
constraint_func = conbytes
|
|
elif issubclass(type_, numeric_types) and not issubclass(
|
|
type_,
|
|
(
|
|
ConstrainedInt,
|
|
ConstrainedFloat,
|
|
ConstrainedDecimal,
|
|
ConstrainedList,
|
|
ConstrainedSet,
|
|
ConstrainedFrozenSet,
|
|
bool,
|
|
),
|
|
):
|
|
# Is numeric type
|
|
attrs = ('gt', 'lt', 'ge', 'le', 'multiple_of')
|
|
if issubclass(type_, float):
|
|
attrs += ('allow_inf_nan',)
|
|
if issubclass(type_, Decimal):
|
|
attrs += ('max_digits', 'decimal_places')
|
|
numeric_type = next(t for t in numeric_types if issubclass(type_, t)) # pragma: no branch
|
|
constraint_func = _map_types_constraint[numeric_type]
|
|
|
|
if attrs:
|
|
used_constraints.update(set(attrs))
|
|
kwargs = {
|
|
attr_name: attr
|
|
for attr_name, attr in ((attr_name, getattr(field_info, attr_name)) for attr_name in attrs)
|
|
if attr is not None
|
|
}
|
|
if kwargs:
|
|
constraint_func = cast(Callable[..., type], constraint_func)
|
|
return constraint_func(**kwargs)
|
|
return type_
|
|
|
|
return go(annotation), used_constraints
|
|
|
|
|
|
def normalize_name(name: str) -> str:
|
|
"""
|
|
Normalizes the given name. This can be applied to either a model *or* enum.
|
|
"""
|
|
return re.sub(r'[^a-zA-Z0-9.\-_]', '_', name)
|
|
|
|
|
|
class SkipField(Exception):
|
|
"""
|
|
Utility exception used to exclude fields from schema.
|
|
"""
|
|
|
|
def __init__(self, message: str) -> None:
|
|
self.message = message
|