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import math
import sys
import types
from dataclasses import dataclass
from datetime import tzinfo
from typing import TYPE_CHECKING, Any, Callable, Iterator, Optional, SupportsFloat, SupportsIndex, TypeVar, Union
if sys.version_info < (3, 8):
from typing_extensions import Protocol, runtime_checkable
else:
from typing import Protocol, runtime_checkable
if sys.version_info < (3, 9):
from typing_extensions import Annotated, Literal
else:
from typing import Annotated, Literal
if sys.version_info < (3, 10):
EllipsisType = type(Ellipsis)
KW_ONLY = {}
SLOTS = {}
else:
from types import EllipsisType
KW_ONLY = {"kw_only": True}
SLOTS = {"slots": True}
__all__ = (
'BaseMetadata',
'GroupedMetadata',
'Gt',
'Ge',
'Lt',
'Le',
'Interval',
'MultipleOf',
'MinLen',
'MaxLen',
'Len',
'Timezone',
'Predicate',
'LowerCase',
'UpperCase',
'IsDigits',
'IsFinite',
'IsNotFinite',
'IsNan',
'IsNotNan',
'IsInfinite',
'IsNotInfinite',
'doc',
'DocInfo',
'__version__',
)
__version__ = '0.7.0'
T = TypeVar('T')
# arguments that start with __ are considered
# positional only
# see https://peps.python.org/pep-0484/#positional-only-arguments
class SupportsGt(Protocol):
def __gt__(self: T, __other: T) -> bool:
...
class SupportsGe(Protocol):
def __ge__(self: T, __other: T) -> bool:
...
class SupportsLt(Protocol):
def __lt__(self: T, __other: T) -> bool:
...
class SupportsLe(Protocol):
def __le__(self: T, __other: T) -> bool:
...
class SupportsMod(Protocol):
def __mod__(self: T, __other: T) -> T:
...
class SupportsDiv(Protocol):
def __div__(self: T, __other: T) -> T:
...
class BaseMetadata:
"""Base class for all metadata.
This exists mainly so that implementers
can do `isinstance(..., BaseMetadata)` while traversing field annotations.
"""
__slots__ = ()
@dataclass(frozen=True, **SLOTS)
class Gt(BaseMetadata):
"""Gt(gt=x) implies that the value must be greater than x.
It can be used with any type that supports the ``>`` operator,
including numbers, dates and times, strings, sets, and so on.
"""
gt: SupportsGt
@dataclass(frozen=True, **SLOTS)
class Ge(BaseMetadata):
"""Ge(ge=x) implies that the value must be greater than or equal to x.
It can be used with any type that supports the ``>=`` operator,
including numbers, dates and times, strings, sets, and so on.
"""
ge: SupportsGe
@dataclass(frozen=True, **SLOTS)
class Lt(BaseMetadata):
"""Lt(lt=x) implies that the value must be less than x.
It can be used with any type that supports the ``<`` operator,
including numbers, dates and times, strings, sets, and so on.
"""
lt: SupportsLt
@dataclass(frozen=True, **SLOTS)
class Le(BaseMetadata):
"""Le(le=x) implies that the value must be less than or equal to x.
It can be used with any type that supports the ``<=`` operator,
including numbers, dates and times, strings, sets, and so on.
"""
le: SupportsLe
@runtime_checkable
class GroupedMetadata(Protocol):
"""A grouping of multiple objects, like typing.Unpack.
`GroupedMetadata` on its own is not metadata and has no meaning.
All of the constraints and metadata should be fully expressable
in terms of the `BaseMetadata`'s returned by `GroupedMetadata.__iter__()`.
Concrete implementations should override `GroupedMetadata.__iter__()`
to add their own metadata.
For example:
>>> @dataclass
>>> class Field(GroupedMetadata):
>>> gt: float | None = None
>>> description: str | None = None
...
>>> def __iter__(self) -> Iterable[object]:
>>> if self.gt is not None:
>>> yield Gt(self.gt)
>>> if self.description is not None:
>>> yield Description(self.gt)
Also see the implementation of `Interval` below for an example.
Parsers should recognize this and unpack it so that it can be used
both with and without unpacking:
- `Annotated[int, Field(...)]` (parser must unpack Field)
- `Annotated[int, *Field(...)]` (PEP-646)
""" # noqa: trailing-whitespace
@property
def __is_annotated_types_grouped_metadata__(self) -> Literal[True]:
return True
def __iter__(self) -> Iterator[object]:
...
if not TYPE_CHECKING:
__slots__ = () # allow subclasses to use slots
def __init_subclass__(cls, *args: Any, **kwargs: Any) -> None:
# Basic ABC like functionality without the complexity of an ABC
super().__init_subclass__(*args, **kwargs)
if cls.__iter__ is GroupedMetadata.__iter__:
raise TypeError("Can't subclass GroupedMetadata without implementing __iter__")
def __iter__(self) -> Iterator[object]: # noqa: F811
raise NotImplementedError # more helpful than "None has no attribute..." type errors
@dataclass(frozen=True, **KW_ONLY, **SLOTS)
class Interval(GroupedMetadata):
"""Interval can express inclusive or exclusive bounds with a single object.
It accepts keyword arguments ``gt``, ``ge``, ``lt``, and/or ``le``, which
are interpreted the same way as the single-bound constraints.
"""
gt: Union[SupportsGt, None] = None
ge: Union[SupportsGe, None] = None
lt: Union[SupportsLt, None] = None
le: Union[SupportsLe, None] = None
def __iter__(self) -> Iterator[BaseMetadata]:
"""Unpack an Interval into zero or more single-bounds."""
if self.gt is not None:
yield Gt(self.gt)
if self.ge is not None:
yield Ge(self.ge)
if self.lt is not None:
yield Lt(self.lt)
if self.le is not None:
yield Le(self.le)
@dataclass(frozen=True, **SLOTS)
class MultipleOf(BaseMetadata):
"""MultipleOf(multiple_of=x) might be interpreted in two ways:
1. Python semantics, implying ``value % multiple_of == 0``, or
2. JSONschema semantics, where ``int(value / multiple_of) == value / multiple_of``
We encourage users to be aware of these two common interpretations,
and libraries to carefully document which they implement.
"""
multiple_of: Union[SupportsDiv, SupportsMod]
@dataclass(frozen=True, **SLOTS)
class MinLen(BaseMetadata):
"""
MinLen() implies minimum inclusive length,
e.g. ``len(value) >= min_length``.
"""
min_length: Annotated[int, Ge(0)]
@dataclass(frozen=True, **SLOTS)
class MaxLen(BaseMetadata):
"""
MaxLen() implies maximum inclusive length,
e.g. ``len(value) <= max_length``.
"""
max_length: Annotated[int, Ge(0)]
@dataclass(frozen=True, **SLOTS)
class Len(GroupedMetadata):
"""
Len() implies that ``min_length <= len(value) <= max_length``.
Upper bound may be omitted or ``None`` to indicate no upper length bound.
"""
min_length: Annotated[int, Ge(0)] = 0
max_length: Optional[Annotated[int, Ge(0)]] = None
def __iter__(self) -> Iterator[BaseMetadata]:
"""Unpack a Len into zone or more single-bounds."""
if self.min_length > 0:
yield MinLen(self.min_length)
if self.max_length is not None:
yield MaxLen(self.max_length)
@dataclass(frozen=True, **SLOTS)
class Timezone(BaseMetadata):
"""Timezone(tz=...) requires a datetime to be aware (or ``tz=None``, naive).
``Annotated[datetime, Timezone(None)]`` must be a naive datetime.
``Timezone[...]`` (the ellipsis literal) expresses that the datetime must be
tz-aware but any timezone is allowed.
You may also pass a specific timezone string or tzinfo object such as
``Timezone(timezone.utc)`` or ``Timezone("Africa/Abidjan")`` to express that
you only allow a specific timezone, though we note that this is often
a symptom of poor design.
"""
tz: Union[str, tzinfo, EllipsisType, None]
@dataclass(frozen=True, **SLOTS)
class Unit(BaseMetadata):
"""Indicates that the value is a physical quantity with the specified unit.
It is intended for usage with numeric types, where the value represents the
magnitude of the quantity. For example, ``distance: Annotated[float, Unit('m')]``
or ``speed: Annotated[float, Unit('m/s')]``.
Interpretation of the unit string is left to the discretion of the consumer.
It is suggested to follow conventions established by python libraries that work
with physical quantities, such as
- ``pint`` : <https://pint.readthedocs.io/en/stable/>
- ``astropy.units``: <https://docs.astropy.org/en/stable/units/>
For indicating a quantity with a certain dimensionality but without a specific unit
it is recommended to use square brackets, e.g. `Annotated[float, Unit('[time]')]`.
Note, however, ``annotated_types`` itself makes no use of the unit string.
"""
unit: str
@dataclass(frozen=True, **SLOTS)
class Predicate(BaseMetadata):
"""``Predicate(func: Callable)`` implies `func(value)` is truthy for valid values.
Users should prefer statically inspectable metadata, but if you need the full
power and flexibility of arbitrary runtime predicates... here it is.
We provide a few predefined predicates for common string constraints:
``IsLower = Predicate(str.islower)``, ``IsUpper = Predicate(str.isupper)``, and
``IsDigits = Predicate(str.isdigit)``. Users are encouraged to use methods which
can be given special handling, and avoid indirection like ``lambda s: s.lower()``.
Some libraries might have special logic to handle certain predicates, e.g. by
checking for `str.isdigit` and using its presence to both call custom logic to
enforce digit-only strings, and customise some generated external schema.
We do not specify what behaviour should be expected for predicates that raise
an exception. For example `Annotated[int, Predicate(str.isdigit)]` might silently
skip invalid constraints, or statically raise an error; or it might try calling it
and then propagate or discard the resulting exception.
"""
func: Callable[[Any], bool]
def __repr__(self) -> str:
if getattr(self.func, "__name__", "<lambda>") == "<lambda>":
return f"{self.__class__.__name__}({self.func!r})"
if isinstance(self.func, (types.MethodType, types.BuiltinMethodType)) and (
namespace := getattr(self.func.__self__, "__name__", None)
):
return f"{self.__class__.__name__}({namespace}.{self.func.__name__})"
if isinstance(self.func, type(str.isascii)): # method descriptor
return f"{self.__class__.__name__}({self.func.__qualname__})"
return f"{self.__class__.__name__}({self.func.__name__})"
@dataclass
class Not:
func: Callable[[Any], bool]
def __call__(self, __v: Any) -> bool:
return not self.func(__v)
_StrType = TypeVar("_StrType", bound=str)
LowerCase = Annotated[_StrType, Predicate(str.islower)]
"""
Return True if the string is a lowercase string, False otherwise.
A string is lowercase if all cased characters in the string are lowercase and there is at least one cased character in the string.
""" # noqa: E501
UpperCase = Annotated[_StrType, Predicate(str.isupper)]
"""
Return True if the string is an uppercase string, False otherwise.
A string is uppercase if all cased characters in the string are uppercase and there is at least one cased character in the string.
""" # noqa: E501
IsDigit = Annotated[_StrType, Predicate(str.isdigit)]
IsDigits = IsDigit # type: ignore # plural for backwards compatibility, see #63
"""
Return True if the string is a digit string, False otherwise.
A string is a digit string if all characters in the string are digits and there is at least one character in the string.
""" # noqa: E501
IsAscii = Annotated[_StrType, Predicate(str.isascii)]
"""
Return True if all characters in the string are ASCII, False otherwise.
ASCII characters have code points in the range U+0000-U+007F. Empty string is ASCII too.
"""
_NumericType = TypeVar('_NumericType', bound=Union[SupportsFloat, SupportsIndex])
IsFinite = Annotated[_NumericType, Predicate(math.isfinite)]
"""Return True if x is neither an infinity nor a NaN, and False otherwise."""
IsNotFinite = Annotated[_NumericType, Predicate(Not(math.isfinite))]
"""Return True if x is one of infinity or NaN, and False otherwise"""
IsNan = Annotated[_NumericType, Predicate(math.isnan)]
"""Return True if x is a NaN (not a number), and False otherwise."""
IsNotNan = Annotated[_NumericType, Predicate(Not(math.isnan))]
"""Return True if x is anything but NaN (not a number), and False otherwise."""
IsInfinite = Annotated[_NumericType, Predicate(math.isinf)]
"""Return True if x is a positive or negative infinity, and False otherwise."""
IsNotInfinite = Annotated[_NumericType, Predicate(Not(math.isinf))]
"""Return True if x is neither a positive or negative infinity, and False otherwise."""
try:
from typing_extensions import DocInfo, doc # type: ignore [attr-defined]
except ImportError:
@dataclass(frozen=True, **SLOTS)
class DocInfo: # type: ignore [no-redef]
""" "
The return value of doc(), mainly to be used by tools that want to extract the
Annotated documentation at runtime.
"""
documentation: str
"""The documentation string passed to doc()."""
def doc(
documentation: str,
) -> DocInfo:
"""
Add documentation to a type annotation inside of Annotated.
For example:
>>> def hi(name: Annotated[int, doc("The name of the user")]) -> None: ...
"""
return DocInfo(documentation)
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