Current File : //opt/cloudlinux/venv/lib/python3.11/site-packages/pydantic/v1/generics.py
import sys
import types
import typing
from typing import (
TYPE_CHECKING,
Any,
ClassVar,
Dict,
ForwardRef,
Generic,
Iterator,
List,
Mapping,
Optional,
Tuple,
Type,
TypeVar,
Union,
cast,
)
from weakref import WeakKeyDictionary, WeakValueDictionary
from typing_extensions import Annotated, Literal as ExtLiteral
from .class_validators import gather_all_validators
from .fields import DeferredType
from .main import BaseModel, create_model
from .types import JsonWrapper
from .typing import display_as_type, get_all_type_hints, get_args, get_origin, typing_base
from .utils import all_identical, lenient_issubclass
if sys.version_info >= (3, 10):
from typing import _UnionGenericAlias
if sys.version_info >= (3, 8):
from typing import Literal
GenericModelT = TypeVar('GenericModelT', bound='GenericModel')
TypeVarType = Any # since mypy doesn't allow the use of TypeVar as a type
CacheKey = Tuple[Type[Any], Any, Tuple[Any, ...]]
Parametrization = Mapping[TypeVarType, Type[Any]]
# weak dictionaries allow the dynamically created parametrized versions of generic models to get collected
# once they are no longer referenced by the caller.
if sys.version_info >= (3, 9): # Typing for weak dictionaries available at 3.9
GenericTypesCache = WeakValueDictionary[CacheKey, Type[BaseModel]]
AssignedParameters = WeakKeyDictionary[Type[BaseModel], Parametrization]
else:
GenericTypesCache = WeakValueDictionary
AssignedParameters = WeakKeyDictionary
# _generic_types_cache is a Mapping from __class_getitem__ arguments to the parametrized version of generic models.
# This ensures multiple calls of e.g. A[B] return always the same class.
_generic_types_cache = GenericTypesCache()
# _assigned_parameters is a Mapping from parametrized version of generic models to assigned types of parametrizations
# as captured during construction of the class (not instances).
# E.g., for generic model `Model[A, B]`, when parametrized model `Model[int, str]` is created,
# `Model[int, str]`: {A: int, B: str}` will be stored in `_assigned_parameters`.
# (This information is only otherwise available after creation from the class name string).
_assigned_parameters = AssignedParameters()
class GenericModel(BaseModel):
__slots__ = ()
__concrete__: ClassVar[bool] = False
if TYPE_CHECKING:
# Putting this in a TYPE_CHECKING block allows us to replace `if Generic not in cls.__bases__` with
# `not hasattr(cls, "__parameters__")`. This means we don't need to force non-concrete subclasses of
# `GenericModel` to also inherit from `Generic`, which would require changes to the use of `create_model` below.
__parameters__: ClassVar[Tuple[TypeVarType, ...]]
# Setting the return type as Type[Any] instead of Type[BaseModel] prevents PyCharm warnings
def __class_getitem__(cls: Type[GenericModelT], params: Union[Type[Any], Tuple[Type[Any], ...]]) -> Type[Any]:
"""Instantiates a new class from a generic class `cls` and type variables `params`.
:param params: Tuple of types the class . Given a generic class
`Model` with 2 type variables and a concrete model `Model[str, int]`,
the value `(str, int)` would be passed to `params`.
:return: New model class inheriting from `cls` with instantiated
types described by `params`. If no parameters are given, `cls` is
returned as is.
"""
def _cache_key(_params: Any) -> CacheKey:
args = get_args(_params)
# python returns a list for Callables, which is not hashable
if len(args) == 2 and isinstance(args[0], list):
args = (tuple(args[0]), args[1])
return cls, _params, args
cached = _generic_types_cache.get(_cache_key(params))
if cached is not None:
return cached
if cls.__concrete__ and Generic not in cls.__bases__:
raise TypeError('Cannot parameterize a concrete instantiation of a generic model')
if not isinstance(params, tuple):
params = (params,)
if cls is GenericModel and any(isinstance(param, TypeVar) for param in params):
raise TypeError('Type parameters should be placed on typing.Generic, not GenericModel')
if not hasattr(cls, '__parameters__'):
raise TypeError(f'Type {cls.__name__} must inherit from typing.Generic before being parameterized')
check_parameters_count(cls, params)
# Build map from generic typevars to passed params
typevars_map: Dict[TypeVarType, Type[Any]] = dict(zip(cls.__parameters__, params))
if all_identical(typevars_map.keys(), typevars_map.values()) and typevars_map:
return cls # if arguments are equal to parameters it's the same object
# Create new model with original model as parent inserting fields with DeferredType.
model_name = cls.__concrete_name__(params)
validators = gather_all_validators(cls)
type_hints = get_all_type_hints(cls).items()
instance_type_hints = {k: v for k, v in type_hints if get_origin(v) is not ClassVar}
fields = {k: (DeferredType(), cls.__fields__[k].field_info) for k in instance_type_hints if k in cls.__fields__}
model_module, called_globally = get_caller_frame_info()
created_model = cast(
Type[GenericModel], # casting ensures mypy is aware of the __concrete__ and __parameters__ attributes
create_model(
model_name,
__module__=model_module or cls.__module__,
__base__=(cls,) + tuple(cls.__parameterized_bases__(typevars_map)),
__config__=None,
__validators__=validators,
__cls_kwargs__=None,
**fields,
),
)
_assigned_parameters[created_model] = typevars_map
if called_globally: # create global reference and therefore allow pickling
object_by_reference = None
reference_name = model_name
reference_module_globals = sys.modules[created_model.__module__].__dict__
while object_by_reference is not created_model:
object_by_reference = reference_module_globals.setdefault(reference_name, created_model)
reference_name += '_'
created_model.Config = cls.Config
# Find any typevars that are still present in the model.
# If none are left, the model is fully "concrete", otherwise the new
# class is a generic class as well taking the found typevars as
# parameters.
new_params = tuple(
{param: None for param in iter_contained_typevars(typevars_map.values())}
) # use dict as ordered set
created_model.__concrete__ = not new_params
if new_params:
created_model.__parameters__ = new_params
# Save created model in cache so we don't end up creating duplicate
# models that should be identical.
_generic_types_cache[_cache_key(params)] = created_model
if len(params) == 1:
_generic_types_cache[_cache_key(params[0])] = created_model
# Recursively walk class type hints and replace generic typevars
# with concrete types that were passed.
_prepare_model_fields(created_model, fields, instance_type_hints, typevars_map)
return created_model
@classmethod
def __concrete_name__(cls: Type[Any], params: Tuple[Type[Any], ...]) -> str:
"""Compute class name for child classes.
:param params: Tuple of types the class . Given a generic class
`Model` with 2 type variables and a concrete model `Model[str, int]`,
the value `(str, int)` would be passed to `params`.
:return: String representing a the new class where `params` are
passed to `cls` as type variables.
This method can be overridden to achieve a custom naming scheme for GenericModels.
"""
param_names = [display_as_type(param) for param in params]
params_component = ', '.join(param_names)
return f'{cls.__name__}[{params_component}]'
@classmethod
def __parameterized_bases__(cls, typevars_map: Parametrization) -> Iterator[Type[Any]]:
"""
Returns unbound bases of cls parameterised to given type variables
:param typevars_map: Dictionary of type applications for binding subclasses.
Given a generic class `Model` with 2 type variables [S, T]
and a concrete model `Model[str, int]`,
the value `{S: str, T: int}` would be passed to `typevars_map`.
:return: an iterator of generic sub classes, parameterised by `typevars_map`
and other assigned parameters of `cls`
e.g.:
```
class A(GenericModel, Generic[T]):
...
class B(A[V], Generic[V]):
...
assert A[int] in B.__parameterized_bases__({V: int})
```
"""
def build_base_model(
base_model: Type[GenericModel], mapped_types: Parametrization
) -> Iterator[Type[GenericModel]]:
base_parameters = tuple(mapped_types[param] for param in base_model.__parameters__)
parameterized_base = base_model.__class_getitem__(base_parameters)
if parameterized_base is base_model or parameterized_base is cls:
# Avoid duplication in MRO
return
yield parameterized_base
for base_model in cls.__bases__:
if not issubclass(base_model, GenericModel):
# not a class that can be meaningfully parameterized
continue
elif not getattr(base_model, '__parameters__', None):
# base_model is "GenericModel" (and has no __parameters__)
# or
# base_model is already concrete, and will be included transitively via cls.
continue
elif cls in _assigned_parameters:
if base_model in _assigned_parameters:
# cls is partially parameterised but not from base_model
# e.g. cls = B[S], base_model = A[S]
# B[S][int] should subclass A[int], (and will be transitively via B[int])
# but it's not viable to consistently subclass types with arbitrary construction
# So don't attempt to include A[S][int]
continue
else: # base_model not in _assigned_parameters:
# cls is partially parameterized, base_model is original generic
# e.g. cls = B[str, T], base_model = B[S, T]
# Need to determine the mapping for the base_model parameters
mapped_types: Parametrization = {
key: typevars_map.get(value, value) for key, value in _assigned_parameters[cls].items()
}
yield from build_base_model(base_model, mapped_types)
else:
# cls is base generic, so base_class has a distinct base
# can construct the Parameterised base model using typevars_map directly
yield from build_base_model(base_model, typevars_map)
def replace_types(type_: Any, type_map: Mapping[Any, Any]) -> Any:
"""Return type with all occurrences of `type_map` keys recursively replaced with their values.
:param type_: Any type, class or generic alias
:param type_map: Mapping from `TypeVar` instance to concrete types.
:return: New type representing the basic structure of `type_` with all
`typevar_map` keys recursively replaced.
>>> replace_types(Tuple[str, Union[List[str], float]], {str: int})
Tuple[int, Union[List[int], float]]
"""
if not type_map:
return type_
type_args = get_args(type_)
origin_type = get_origin(type_)
if origin_type is Annotated:
annotated_type, *annotations = type_args
return Annotated[replace_types(annotated_type, type_map), tuple(annotations)]
if (origin_type is ExtLiteral) or (sys.version_info >= (3, 8) and origin_type is Literal):
return type_map.get(type_, type_)
# Having type args is a good indicator that this is a typing module
# class instantiation or a generic alias of some sort.
if type_args:
resolved_type_args = tuple(replace_types(arg, type_map) for arg in type_args)
if all_identical(type_args, resolved_type_args):
# If all arguments are the same, there is no need to modify the
# type or create a new object at all
return type_
if (
origin_type is not None
and isinstance(type_, typing_base)
and not isinstance(origin_type, typing_base)
and getattr(type_, '_name', None) is not None
):
# In python < 3.9 generic aliases don't exist so any of these like `list`,
# `type` or `collections.abc.Callable` need to be translated.
# See: https://www.python.org/dev/peps/pep-0585
origin_type = getattr(typing, type_._name)
assert origin_type is not None
# PEP-604 syntax (Ex.: list | str) is represented with a types.UnionType object that does not have __getitem__.
# We also cannot use isinstance() since we have to compare types.
if sys.version_info >= (3, 10) and origin_type is types.UnionType: # noqa: E721
return _UnionGenericAlias(origin_type, resolved_type_args)
return origin_type[resolved_type_args]
# We handle pydantic generic models separately as they don't have the same
# semantics as "typing" classes or generic aliases
if not origin_type and lenient_issubclass(type_, GenericModel) and not type_.__concrete__:
type_args = type_.__parameters__
resolved_type_args = tuple(replace_types(t, type_map) for t in type_args)
if all_identical(type_args, resolved_type_args):
return type_
return type_[resolved_type_args]
# Handle special case for typehints that can have lists as arguments.
# `typing.Callable[[int, str], int]` is an example for this.
if isinstance(type_, (List, list)):
resolved_list = list(replace_types(element, type_map) for element in type_)
if all_identical(type_, resolved_list):
return type_
return resolved_list
# For JsonWrapperValue, need to handle its inner type to allow correct parsing
# of generic Json arguments like Json[T]
if not origin_type and lenient_issubclass(type_, JsonWrapper):
type_.inner_type = replace_types(type_.inner_type, type_map)
return type_
# If all else fails, we try to resolve the type directly and otherwise just
# return the input with no modifications.
new_type = type_map.get(type_, type_)
# Convert string to ForwardRef
if isinstance(new_type, str):
return ForwardRef(new_type)
else:
return new_type
def check_parameters_count(cls: Type[GenericModel], parameters: Tuple[Any, ...]) -> None:
actual = len(parameters)
expected = len(cls.__parameters__)
if actual != expected:
description = 'many' if actual > expected else 'few'
raise TypeError(f'Too {description} parameters for {cls.__name__}; actual {actual}, expected {expected}')
DictValues: Type[Any] = {}.values().__class__
def iter_contained_typevars(v: Any) -> Iterator[TypeVarType]:
"""Recursively iterate through all subtypes and type args of `v` and yield any typevars that are found."""
if isinstance(v, TypeVar):
yield v
elif hasattr(v, '__parameters__') and not get_origin(v) and lenient_issubclass(v, GenericModel):
yield from v.__parameters__
elif isinstance(v, (DictValues, list)):
for var in v:
yield from iter_contained_typevars(var)
else:
args = get_args(v)
for arg in args:
yield from iter_contained_typevars(arg)
def get_caller_frame_info() -> Tuple[Optional[str], bool]:
"""
Used inside a function to check whether it was called globally
Will only work against non-compiled code, therefore used only in pydantic.generics
:returns Tuple[module_name, called_globally]
"""
try:
previous_caller_frame = sys._getframe(2)
except ValueError as e:
raise RuntimeError('This function must be used inside another function') from e
except AttributeError: # sys module does not have _getframe function, so there's nothing we can do about it
return None, False
frame_globals = previous_caller_frame.f_globals
return frame_globals.get('__name__'), previous_caller_frame.f_locals is frame_globals
def _prepare_model_fields(
created_model: Type[GenericModel],
fields: Mapping[str, Any],
instance_type_hints: Mapping[str, type],
typevars_map: Mapping[Any, type],
) -> None:
"""
Replace DeferredType fields with concrete type hints and prepare them.
"""
for key, field in created_model.__fields__.items():
if key not in fields:
assert field.type_.__class__ is not DeferredType
# https://github.com/nedbat/coveragepy/issues/198
continue # pragma: no cover
assert field.type_.__class__ is DeferredType, field.type_.__class__
field_type_hint = instance_type_hints[key]
concrete_type = replace_types(field_type_hint, typevars_map)
field.type_ = concrete_type
field.outer_type_ = concrete_type
field.prepare()
created_model.__annotations__[key] = concrete_type
Mini Shell