AlkantarClanX12
Your IP : 18.191.178.16
import re
import warnings
from collections import defaultdict
from dataclasses import is_dataclass
from datetime import date, datetime, time, timedelta
from decimal import Decimal
from enum import Enum
from ipaddress import IPv4Address, IPv4Interface, IPv4Network, IPv6Address, IPv6Interface, IPv6Network
from pathlib import Path
from typing import (
TYPE_CHECKING,
Any,
Callable,
Dict,
ForwardRef,
FrozenSet,
Generic,
Iterable,
List,
Optional,
Pattern,
Sequence,
Set,
Tuple,
Type,
TypeVar,
Union,
cast,
)
from uuid import UUID
from typing_extensions import Annotated, Literal
from pydantic.v1.fields import (
MAPPING_LIKE_SHAPES,
SHAPE_DEQUE,
SHAPE_FROZENSET,
SHAPE_GENERIC,
SHAPE_ITERABLE,
SHAPE_LIST,
SHAPE_SEQUENCE,
SHAPE_SET,
SHAPE_SINGLETON,
SHAPE_TUPLE,
SHAPE_TUPLE_ELLIPSIS,
FieldInfo,
ModelField,
)
from pydantic.v1.json import pydantic_encoder
from pydantic.v1.networks import AnyUrl, EmailStr
from pydantic.v1.types import (
ConstrainedDecimal,
ConstrainedFloat,
ConstrainedFrozenSet,
ConstrainedInt,
ConstrainedList,
ConstrainedSet,
ConstrainedStr,
SecretBytes,
SecretStr,
StrictBytes,
StrictStr,
conbytes,
condecimal,
confloat,
confrozenset,
conint,
conlist,
conset,
constr,
)
from pydantic.v1.typing import (
all_literal_values,
get_args,
get_origin,
get_sub_types,
is_callable_type,
is_literal_type,
is_namedtuple,
is_none_type,
is_union,
)
from pydantic.v1.utils import ROOT_KEY, get_model, lenient_issubclass
if TYPE_CHECKING:
from pydantic.v1.dataclasses import Dataclass
from pydantic.v1.main import BaseModel
default_prefix = '#/definitions/'
default_ref_template = '#/definitions/{model}'
TypeModelOrEnum = Union[Type['BaseModel'], Type[Enum]]
TypeModelSet = Set[TypeModelOrEnum]
def _apply_modify_schema(
modify_schema: Callable[..., None], field: Optional[ModelField], field_schema: Dict[str, Any]
) -> None:
from inspect import signature
sig = signature(modify_schema)
args = set(sig.parameters.keys())
if 'field' in args or 'kwargs' in args:
modify_schema(field_schema, field=field)
else:
modify_schema(field_schema)
def schema(
models: Sequence[Union[Type['BaseModel'], Type['Dataclass']]],
*,
by_alias: bool = True,
title: Optional[str] = None,
description: Optional[str] = None,
ref_prefix: Optional[str] = None,
ref_template: str = default_ref_template,
) -> Dict[str, Any]:
"""
Process a list of models and generate a single JSON Schema with all of them defined in the ``definitions``
top-level JSON key, including their sub-models.
:param models: a list of models to include in the generated JSON Schema
:param by_alias: generate the schemas using the aliases defined, if any
:param title: title for the generated schema that includes the definitions
:param description: description for the generated schema
:param ref_prefix: the JSON Pointer prefix for schema references with ``$ref``, if None, will be set to the
default of ``#/definitions/``. Update it if you want the schemas to reference the definitions somewhere
else, e.g. for OpenAPI use ``#/components/schemas/``. The resulting generated schemas will still be at the
top-level key ``definitions``, so you can extract them from there. But all the references will have the set
prefix.
:param ref_template: Use a ``string.format()`` template for ``$ref`` instead of a prefix. This can be useful
for references that cannot be represented by ``ref_prefix`` such as a definition stored in another file. For
a sibling json file in a ``/schemas`` directory use ``"/schemas/${model}.json#"``.
:return: dict with the JSON Schema with a ``definitions`` top-level key including the schema definitions for
the models and sub-models passed in ``models``.
"""
clean_models = [get_model(model) for model in models]
flat_models = get_flat_models_from_models(clean_models)
model_name_map = get_model_name_map(flat_models)
definitions = {}
output_schema: Dict[str, Any] = {}
if title:
output_schema['title'] = title
if description:
output_schema['description'] = description
for model in clean_models:
m_schema, m_definitions, m_nested_models = model_process_schema(
model,
by_alias=by_alias,
model_name_map=model_name_map,
ref_prefix=ref_prefix,
ref_template=ref_template,
)
definitions.update(m_definitions)
model_name = model_name_map[model]
definitions[model_name] = m_schema
if definitions:
output_schema['definitions'] = definitions
return output_schema
def model_schema(
model: Union[Type['BaseModel'], Type['Dataclass']],
by_alias: bool = True,
ref_prefix: Optional[str] = None,
ref_template: str = default_ref_template,
) -> Dict[str, Any]:
"""
Generate a JSON Schema for one model. With all the sub-models defined in the ``definitions`` top-level
JSON key.
:param model: a Pydantic model (a class that inherits from BaseModel)
:param by_alias: generate the schemas using the aliases defined, if any
:param ref_prefix: the JSON Pointer prefix for schema references with ``$ref``, if None, will be set to the
default of ``#/definitions/``. Update it if you want the schemas to reference the definitions somewhere
else, e.g. for OpenAPI use ``#/components/schemas/``. The resulting generated schemas will still be at the
top-level key ``definitions``, so you can extract them from there. But all the references will have the set
prefix.
:param ref_template: Use a ``string.format()`` template for ``$ref`` instead of a prefix. This can be useful for
references that cannot be represented by ``ref_prefix`` such as a definition stored in another file. For a
sibling json file in a ``/schemas`` directory use ``"/schemas/${model}.json#"``.
:return: dict with the JSON Schema for the passed ``model``
"""
model = get_model(model)
flat_models = get_flat_models_from_model(model)
model_name_map = get_model_name_map(flat_models)
model_name = model_name_map[model]
m_schema, m_definitions, nested_models = model_process_schema(
model, by_alias=by_alias, model_name_map=model_name_map, ref_prefix=ref_prefix, ref_template=ref_template
)
if model_name in nested_models:
# model_name is in Nested models, it has circular references
m_definitions[model_name] = m_schema
m_schema = get_schema_ref(model_name, ref_prefix, ref_template, False)
if m_definitions:
m_schema.update({'definitions': m_definitions})
return m_schema
def get_field_info_schema(field: ModelField, schema_overrides: bool = False) -> Tuple[Dict[str, Any], bool]:
# If no title is explicitly set, we don't set title in the schema for enums.
# The behaviour is the same as `BaseModel` reference, where the default title
# is in the definitions part of the schema.
schema_: Dict[str, Any] = {}
if field.field_info.title or not lenient_issubclass(field.type_, Enum):
schema_['title'] = field.field_info.title or field.alias.title().replace('_', ' ')
if field.field_info.title:
schema_overrides = True
if field.field_info.description:
schema_['description'] = field.field_info.description
schema_overrides = True
if not field.required and field.default is not None and not is_callable_type(field.outer_type_):
schema_['default'] = encode_default(field.default)
schema_overrides = True
return schema_, schema_overrides
def field_schema(
field: ModelField,
*,
by_alias: bool = True,
model_name_map: Dict[TypeModelOrEnum, str],
ref_prefix: Optional[str] = None,
ref_template: str = default_ref_template,
known_models: Optional[TypeModelSet] = None,
) -> Tuple[Dict[str, Any], Dict[str, Any], Set[str]]:
"""
Process a Pydantic field and return a tuple with a JSON Schema for it as the first item.
Also return a dictionary of definitions with models as keys and their schemas as values. If the passed field
is a model and has sub-models, and those sub-models don't have overrides (as ``title``, ``default``, etc), they
will be included in the definitions and referenced in the schema instead of included recursively.
:param field: a Pydantic ``ModelField``
:param by_alias: use the defined alias (if any) in the returned schema
:param model_name_map: used to generate the JSON Schema references to other models included in the definitions
:param ref_prefix: the JSON Pointer prefix to use for references to other schemas, if None, the default of
#/definitions/ will be used
:param ref_template: Use a ``string.format()`` template for ``$ref`` instead of a prefix. This can be useful for
references that cannot be represented by ``ref_prefix`` such as a definition stored in another file. For a
sibling json file in a ``/schemas`` directory use ``"/schemas/${model}.json#"``.
:param known_models: used to solve circular references
:return: tuple of the schema for this field and additional definitions
"""
s, schema_overrides = get_field_info_schema(field)
validation_schema = get_field_schema_validations(field)
if validation_schema:
s.update(validation_schema)
schema_overrides = True
f_schema, f_definitions, f_nested_models = field_type_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 or set(),
)
# $ref will only be returned when there are no schema_overrides
if '$ref' in f_schema:
return f_schema, f_definitions, f_nested_models
else:
s.update(f_schema)
return s, f_definitions, f_nested_models
numeric_types = (int, float, Decimal)
_str_types_attrs: Tuple[Tuple[str, Union[type, Tuple[type, ...]], str], ...] = (
('max_length', numeric_types, 'maxLength'),
('min_length', numeric_types, 'minLength'),
('regex', str, 'pattern'),
)
_numeric_types_attrs: Tuple[Tuple[str, Union[type, Tuple[type, ...]], str], ...] = (
('gt', numeric_types, 'exclusiveMinimum'),
('lt', numeric_types, 'exclusiveMaximum'),
('ge', numeric_types, 'minimum'),
('le', numeric_types, 'maximum'),
('multiple_of', numeric_types, 'multipleOf'),
)
def get_field_schema_validations(field: ModelField) -> Dict[str, Any]:
"""
Get the JSON Schema validation keywords for a ``field`` with an annotation of
a Pydantic ``FieldInfo`` with validation arguments.
"""
f_schema: Dict[str, Any] = {}
if lenient_issubclass(field.type_, Enum):
# schema is already updated by `enum_process_schema`; just update with field extra
if field.field_info.extra:
f_schema.update(field.field_info.extra)
return f_schema
if lenient_issubclass(field.type_, (str, bytes)):
for attr_name, t, keyword in _str_types_attrs:
attr = getattr(field.field_info, attr_name, None)
if isinstance(attr, t):
f_schema[keyword] = attr
if lenient_issubclass(field.type_, numeric_types) and not issubclass(field.type_, bool):
for attr_name, t, keyword in _numeric_types_attrs:
attr = getattr(field.field_info, attr_name, None)
if isinstance(attr, t):
f_schema[keyword] = attr
if field.field_info is not None and field.field_info.const:
f_schema['const'] = field.default
if field.field_info.extra:
f_schema.update(field.field_info.extra)
modify_schema = getattr(field.outer_type_, '__modify_schema__', None)
if modify_schema:
_apply_modify_schema(modify_schema, field, f_schema)
return f_schema
def get_model_name_map(unique_models: TypeModelSet) -> Dict[TypeModelOrEnum, str]:
"""
Process a set of models and generate unique names for them to be used as keys in the JSON Schema
definitions. By default the names are the same as the class name. But if two models in different Python
modules have the same name (e.g. "users.Model" and "items.Model"), the generated names will be
based on the Python module path for those conflicting models to prevent name collisions.
:param unique_models: a Python set of models
:return: dict mapping models to names
"""
name_model_map = {}
conflicting_names: Set[str] = set()
for model in unique_models:
model_name = normalize_name(model.__name__)
if model_name in conflicting_names:
model_name = get_long_model_name(model)
name_model_map[model_name] = model
elif model_name in name_model_map:
conflicting_names.add(model_name)
conflicting_model = name_model_map.pop(model_name)
name_model_map[get_long_model_name(conflicting_model)] = conflicting_model
name_model_map[get_long_model_name(model)] = model
else:
name_model_map[model_name] = model
return {v: k for k, v in name_model_map.items()}
def get_flat_models_from_model(model: Type['BaseModel'], known_models: Optional[TypeModelSet] = None) -> TypeModelSet:
"""
Take a single ``model`` and generate a set with itself and all the sub-models in the tree. I.e. if you pass
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``),
the return value will be ``set([Foo, Bar, Baz])``.
:param model: a Pydantic ``BaseModel`` subclass
:param known_models: used to solve circular references
:return: a set with the initial model and all its sub-models
"""
known_models = known_models or set()
flat_models: TypeModelSet = set()
flat_models.add(model)
known_models |= flat_models
fields = cast(Sequence[ModelField], model.__fields__.values())
flat_models |= get_flat_models_from_fields(fields, known_models=known_models)
return flat_models
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 subclass 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 pydantic.v1.main import BaseModel
flat_models: TypeModelSet = set()
field_type = field.type_
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 pydantic.v1.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'] = {ConstrainedStr._get_pattern(regex): items_schema}
if 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: Optional[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.
"""
import inspect
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': inspect.cleandoc(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():
if isinstance(discriminator_value, Enum):
discriminator_value = str(discriminator_value.value)
# 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 pydantic.v1.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 = tuple(x.value if isinstance(x, Enum) else x for x in 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:
from pydantic.v1.main import BaseModel
if isinstance(dft, BaseModel) or is_dataclass(dft):
dft = cast('dict[str, Any]', pydantic_encoder(dft))
if isinstance(dft, dict):
return {encode_default(k): encode_default(v) for k, v in dft.items()}
elif 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 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://docs.pydantic.dev/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]: # noqa: F811
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