type_enforced

Type Enforced

PyPI version License: MIT PyPI Downloads

A pure python (no special compiler required) type enforcer for type annotations. Enforce types in python functions and methods.

Setup

Make sure you have Python 3.11.x (or higher) installed on your system. You can download it here.

  • Unsupported python versions can be used, however newer features will not be available.
    • For 3.7: use type_enforced==0.0.16 (only very basic type checking is supported)
    • For 3.8: use type_enforced==0.0.16 (only very basic type checking is supported)
    • For 3.9: use type_enforced<=1.9.0 (staticmethod, union with | and from __future__ import annotations typechecking are not supported)
    • For 3.10: use type_enforced<=1.10.2 (from __future__ import annotations may cause errors (EG: when using staticmethods and classmethods))

Installation

pip install type_enforced

Basic Usage

import type_enforced

@type_enforced.Enforcer(enabled=True, strict=True)
def my_fn(a: int , b: int | str =2, c: int =3) -> None:
    pass
  • Note: enabled=True by default if not specified. You can set enabled=False to disable type checking for a specific function, method, or class. This is useful for a production vs debugging environment or for undecorating a single method in a larger wrapped class.
  • Note: strict=True by default if not specified. You can set strict=False to disable exceptions being raised when type checking fails. Instead, a warning will be printed to the console.

Getting Started

type_enforcer contains a basic Enforcer wrapper that can be used to enforce many basic python typing hints. Technical Docs Here.

Enforcer can be used as a decorator for functions, methods, and classes. It will enforce the type hints on the function or method inputs and outputs. It takes in the following optional arguments:

  • enabled (True): A boolean to enable or disable type checking. If True, type checking will be enforced. If False, type checking will be disabled.
  • strict (True): A boolean to enable or disable type mismatch exceptions. If True exceptions will be raised when type checking fails. If False, exceptions will not be raised but instead a warning will be printed to the console.

type_enforcer currently supports many single and multi level python types. This includes class instances and classes themselves. For example, you can force an input to be an int, a number int | float, an instance of the self defined MyClass, or a even a vector with list[int]. Items like typing.List, typing.Dict, typing.Union and typing.Optional are supported.

You can pass union types to validate one of multiple types. For example, you could validate an input was an int or a float with int | float or typing.Union[int, float].

Nesting is allowed as long as the nested items are iterables (e.g. typing.List, dict, ...). For example, you could validate that a list is a vector with list[int] or possibly typing.List[int].

Variables without an annotation for type are not enforced.

Why use Type Enforced?

  • type_enforced is a pure python type enforcer that does not require any special compiler or preprocessor to work.
  • type_enforced uses the standard python typing hints and enforces them at runtime.
    • This means that you can use it in any python environment (3.11+) without any special setup.
  • type_enforced is designed to be lightweight and easy to use, making it a great choice for both small and large projects.
  • type_enforced supports complex (nested) typing hints, union types, and many of the standard python typing functions.
  • Other Options:
    • Pydantic:
      • A great all around option.
      • Its ~40% slower at scale than type enforced for larger object verifications with complex (nested) types.
      • It is ~30% faster for verifications of smaller objects.
      • It is designed for data classes
      • We have found it to be very consistent with type checking including complex (nested) type hints.
    • Beartype:
      • The fastest option we tested by far (close to O(n) time complexity).
      • Is great for simple type checking and we highly recommend it for that use case.
      • Is inconsistent at catching errors with complex (nested) type hints (at least in beartype 0.21.0)
        • EG: Running the same data over a loop randomly fails to catch type errors.
    • Typeguard:
      • A useable option.
      • ~100% slower than type enforced for simple type checking.
      • Does not support complex (nested) type hints.
    • Enforce:
      • Has not been updated since 2017.
      • We were unable to get working with python 3.13.
  • Note: See the benchmarks for more information on the performance of each type checker.

Supported Type Checking Features:

  • Function/Method Input Typing
  • Function/Method Return Typing
  • Dataclass Typing
  • All standard python types (str, list, int, dict, ...)
  • Union types
    • typing.Union
    • | separated items (e.g. int | float)
  • Nested types (e.g. dict[str, int] or list[int|float])
    • Note: Each parent level must be an iterable
      • Specifically a variant of list, set, tuple or dict
    • Note: dict requires two types to be specified (unions count as a single type)
      • The first type is the key type and the second type is the value type
      • e.g. dict[str, int|float] or dict[int, float]
    • Note: list and set require a single type to be specified (unions count as a single type)
      • e.g. list[int], set[str], list[float|str]
    • Note: tuple Allows for N types to be specified
      • Each item refers to the positional type of each item in the tuple
      • Support for ellipsis (...) is supported if you only specify two types and the second is the ellipsis type
        • e.g. tuple[int, ...] or tuple[int|str, ...]
      • Note: Unions between two tuples are not supported
        • e.g. tuple[int, str] | tuple[str, int] will not work
    • Deeply nested types are supported too:
      • dict[dict[int]]
      • list[set[str]]
  • Many of the typing (package) functions and methods including:
    • Standard typing functions:
      • List
      • Set
      • Dict
      • Tuple
    • Union
    • Optional
    • Any
    • Sized
      • Essentially creates a union of:
        • list, tuple, dict, set, str, bytes, bytearray, memoryview, range
      • Note: Can not have a nested type
        • Because this does not always meet the criteria for Nested types above
    • Literal
      • Only allow certain values to be passed. Operates slightly differently than other checks.
      • e.g. Literal['a', 'b'] will require any passed values that are equal (==) to 'a' or 'b'.
        • This compares the value of the passed input and not the type of the passed input.
      • Note: Multiple types can be passed in the same Literal as acceptable values.
        • e.g. Literal['a', 'b', 1, 2] will require any passed values that are equal (==) to 'a', 'b', 1 or 2.
      • Note: If type is a str | Literal['a', 'b']
        • The check will validate that the type is a string or the value is equal to 'a' or 'b'.
        • This means that an input of 'c' will pass the check since it matches the string type, but an input of 1 will fail.
      • Note: If type is a int | Literal['a', 'b']
        • The check will validate that the type is an int or the value is equal to 'a' or 'b'.
        • This means that an input of 'c' will fail the check, but an input of 1 will pass.
      • Note: Literals stack when used with unions.
        • e.g. Literal['a', 'b'] | Literal[1, 2] will require any passed values that are equal (==) to 'a', 'b', 1 or 2.
    • Callable
      • Essentially creates a union of:
        • staticmethod, classmethod, types.FunctionType, types.BuiltinFunctionType, types.MethodType, types.BuiltinMethodType, types.GeneratorType
    • Note: Other functions might have support, but there are not currently tests to validate them
      • Feel free to create an issue (or better yet a PR) if you want to add tests/support
  • Constraint validation.
    • This is a special type of validation that allows passed input to be validated.
      • Standard and custom constraints are supported.
    • This is useful for validating that a passed input is within a certain range or meets a certain criteria.
    • Note: Constraints stack when used with unions.
      • e.g. int | Constraint(ge=0) | Constraint(le=5) will require any passed values to be integers that are greater than or equal to 0 and less than or equal to 5.
    • Note: The constraint is checked after type checking occurs and operates independently of the type checking.
      • This operates differently than other checks (like Literal) and is evaluated post type checking.
      • For example, if you have an annotation of str | Constraint(ge=0), this will always raise an exception since if you pass a string, it will raise on the constraint check and if you pass an integer, it will raise on the type check.
    • Note: See the example below or technical constraint and generic constraint docs for more information. ```

Interactive Example

>>> import type_enforced
>>> @type_enforced.Enforcer
... def my_fn(a: int , b: int|str =2, c: int =3) -> None:
...     pass
...
>>> my_fn(a=1, b=2, c=3)
>>> my_fn(a=1, b='2', c=3)
>>> my_fn(a='a', b=2, c=3)
Traceback (most recent call last):
  File "<python-input-2>", line 1, in <module>
    my_fn(a='a', b=2, c=3)
    ~~~~~^^^^^^^^^^^^^^^^^
  File "/app/type_enforced/enforcer.py", line 233, in __call__
    self.__check_type__(assigned_vars.get(key), value, key)
    ~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/app/type_enforced/enforcer.py", line 266, in __check_type__
    self.__exception__(
    ~~~~~~~~~~~~~~~~~~^
        f"Type mismatch for typed variable `{key}`. Expected one of the following `{list(expected.keys())}` but got `{obj_type}` with value `{obj}` instead."
        ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
    )
    ^
  File "/app/type_enforced/enforcer.py", line 188, in __exception__
    raise TypeError(f"TypeEnforced Exception ({self.__fn__.__qualname__}): {message}")
TypeError: TypeEnforced Exception (my_fn): Type mismatch for typed variable `a`. Expected one of the following `[<class 'int'>]` but got `<class 'str'>` with value `a` instead.

Nested Examples

import type_enforced
import typing

@type_enforced.Enforcer
def my_fn(
    a: dict[str,dict[str, int|float]], # Note: For dicts, the key is the first type and the value is the second type
    b: list[typing.Set[str]] # Could also just use set
) -> None:
    return None

my_fn(a={'i':{'j':1}}, b=[{'x'}]) # Success

my_fn(a={'i':{'j':'k'}}, b=[{'x'}]) # Error =>
# TypeError: TypeEnforced Exception (my_fn): Type mismatch for typed variable `a['i']['j']`. Expected one of the following `[<class 'int'>, <class 'float'>]` but got `<class 'str'>` with value `k` instead.

Class and Method Use

Type enforcer can be applied to methods individually:

import type_enforced

class my_class:
    @type_enforced.Enforcer
    def my_fn(self, b:int):
        pass

You can also enforce all typing for all methods in a class by decorating the class itself.

import type_enforced

@type_enforced.Enforcer
class my_class:
    def my_fn(self, b:int):
        pass

    def my_other_fn(self, a: int, b: int | str):
      pass

You can also enforce types on staticmethods and classmethods if you are using python >= 3.10. If you are using a python version less than this, classmethods and staticmethods methods will not have their types enforced.

import type_enforced

@type_enforced.Enforcer
class my_class:
    @classmethod
    def my_fn(self, b:int):
        pass

    @staticmethod
    def my_other_fn(a: int, b: int | str):
      pass

Dataclasses are suported too.

import type_enforced
from dataclasses import dataclass

@type_enforced.Enforcer
@dataclass
class my_class:
    foo: int
    bar: str

You can skip enforcement if you add the argument enabled=False in the Enforcer call.

  • This is useful for a production vs debugging environment.
  • This is also useful for undecorating a single method in a larger wrapped class.
  • Note: You can set enabled=False for an entire class or simply disable a specific method in a larger wrapped class.
  • Note: Method level wrapper enabled values take precedence over class level wrappers.
import type_enforced
@type_enforced.Enforcer
class my_class:
    def my_fn(self, a: int) -> None:
        pass

    @type_enforced.Enforcer(enabled=False)
    def my_other_fn(self, a: int) -> None:
        pass

Validate with Constraints

Type enforcer can enforce constraints for passed variables. These constraints are validated after any type checks are made.

To enforce basic input values are integers greater than or equal to zero, you can use the Constraint class like so:

import type_enforced
from type_enforced.utils import Constraint

@type_enforced.Enforcer()
def positive_int_test(value: int |Constraint(ge=0)) -> bool:
    return True

positive_int_test(1) # Passes
positive_int_test(-1) # Fails
positive_int_test(1.0) # Fails

To enforce a GenericConstraint:

import type_enforced
from type_enforced.utils import GenericConstraint

CustomConstraint = GenericConstraint(
    {
        'in_rgb': lambda x: x in ['red', 'green', 'blue'],
    }
)

@type_enforced.Enforcer()
def rgb_test(value: str | CustomConstraint) -> bool:
    return True

rgb_test('red') # Passes
rgb_test('yellow') # Fails

Validate class instances and classes

Type enforcer can enforce class instances and classes. There are a few caveats between the two.

To enforce a class instance, simply pass the class itself as a type hint:

import type_enforced

class Foo():
    def __init__(self) -> None:
        pass

@type_enforced.Enforcer
class my_class():
    def __init__(self, object: Foo) -> None:
        self.object = object

x=my_class(Foo()) # Works great!
y=my_class(Foo) # Fails!

Notice how an initialized class instance Foo() must be passed for the enforcer to not raise an exception.

To enforce an uninitialized class object use typing.Type[classHere] on the class to enforce inputs to be an uninitialized class:

import type_enforced
import typing

class Foo():
    def __init__(self) -> None:
        pass

@type_enforced.Enforcer
class my_class():
    def __init__(self, object_class: typing.Type[Foo]) -> None:
        self.object = object_class()

y=my_class(Foo) # Works great!
x=my_class(Foo()) # Fails

By default, type_enforced will check for subclasses of a class when validating types. This means that if you pass a subclass of the expected class, it will pass the type check.

Note: Uninitialized class objects that are passed are not checked for subclasses.

import type_enforced

class Foo:
    pass

class Bar(Foo):
    pass

class Baz:
    pass

@type_enforced.Enforcer
def my_fn(custom_class: Foo):
    pass

my_fn(Foo()) # Passes as expected
my_fn(Bar()) # Passes as expected
my_fn(Baz()) # Raises TypeError as expected

What changed in 2.0.0?

The main changes in version 2.0.0 revolve around migrating towards the standard python typing hint process and away from the original type_enfoced type hints (as type enforced was originally created before the | operator was added to python).

  • Support for python3.10 has been dropped.
  • List based union types are no longer supported.
    • For example [int, float] is no longer a supported type hint.
    • Use int|float or typing.Union[int, float] instead.
  • Dict types now require two types to be specified.
    • The first type is the key type and the second type is the value type.
    • For example, dict[str, int|float] or dict[int, float] are valid types.
  • Tuple types now allow for N types to be specified.
    • Each item refers to the positional type of each item in the tuple.
    • Support for ellipsis (...) is supported if you only specify two types and the second is the ellipsis type.
      • For example, tuple[int, ...] or tuple[int|str, ...] are valid types.
    • Note: Unions between two tuples are not supported.
      • For example, tuple[int, str] | tuple[str, int] will not work.
  • Constraints and Literals can now be stacked with unions.
    • For example, int | Constraint(ge=0) | Constraint(le=5) will require any passed values to be integers that are greater than or equal to 0 and less than or equal to 5.
    • For example, Literal['a', 'b'] | Literal[1, 2] will require any passed values that are equal (==) to 'a', 'b', 1 or 2.
  • Literals now evaluate during the same time as type checking and operate as OR checks.
    • For example, int | Literal['a', 'b'] will validate that the type is an int or the value is equal to 'a' or 'b'.
  • Constraints are still are evaluated after type checking and operate independently of the type checking.

Development

Running Tests, Prettifying Code, and Updating Docs

Make sure Docker is installed and running.

  • Create a docker container and drop into a shell
    • ./run.sh
  • Run all tests (see ./utils/test.sh)
    • ./run.sh test
  • Prettify the code (see ./utils/prettify.sh)
    • ./run.sh prettify

  • Update the docs (see ./utils/docs.sh)

    • ./run.sh docs

  • Note: You can and should modify the Dockerfile to test different python versions.

  1"""
  2# Type Enforced
  3[![PyPI version](https://badge.fury.io/py/type_enforced.svg)](https://badge.fury.io/py/type_enforced)
  4[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)
  5[![PyPI Downloads](https://img.shields.io/pypi/dm/type_enforced.svg?label=PyPI%20downloads)](https://pypi.org/project/type_enforced/)
  6
  7A pure python (no special compiler required) type enforcer for type annotations. Enforce types in python functions and methods.
  8
  9# Setup
 10
 11Make sure you have Python 3.11.x (or higher) installed on your system. You can download it [here](https://www.python.org/downloads/).
 12
 13- Unsupported python versions can be used, however newer features will not be available.
 14    - For 3.7: use type_enforced==0.0.16 (only very basic type checking is supported)
 15    - For 3.8: use type_enforced==0.0.16 (only very basic type checking is supported)
 16    - For 3.9: use type_enforced<=1.9.0 (`staticmethod`, union with `|` and `from __future__ import annotations` typechecking are not supported)
 17    - For 3.10: use type_enforced<=1.10.2 (`from __future__ import annotations` may cause errors (EG: when using staticmethods and classmethods))
 18
 19### Installation
 20
 21```
 22pip install type_enforced
 23```
 24
 25## Basic Usage
 26```py
 27import type_enforced
 28
 29@type_enforced.Enforcer(enabled=True, strict=True)
 30def my_fn(a: int , b: int | str =2, c: int =3) -> None:
 31    pass
 32```
 33- Note: `enabled=True` by default if not specified. You can set `enabled=False` to disable type checking for a specific function, method, or class. This is useful for a production vs debugging environment or for undecorating a single method in a larger wrapped class. 
 34- Note: `strict=True` by default if not specified. You can set `strict=False` to disable exceptions being raised when type checking fails. Instead, a warning will be printed to the console.
 35
 36## Getting Started
 37
 38`type_enforcer` contains a basic `Enforcer` wrapper that can be used to enforce many basic python typing hints. [Technical Docs Here](https://connor-makowski.github.io/type_enforced/type_enforced/enforcer.html).
 39
 40`Enforcer` can be used as a decorator for functions, methods, and classes. It will enforce the type hints on the function or method inputs and outputs. It takes in the following optional arguments:
 41
 42- `enabled` (True): A boolean to enable or disable type checking. If `True`, type checking will be enforced. If `False`, type checking will be disabled.
 43- `strict` (True): A boolean to enable or disable type mismatch exceptions. If `True` exceptions will be raised when type checking fails. If `False`, exceptions will not be raised but instead a warning will be printed to the console.
 44
 45`type_enforcer` currently supports many single and multi level python types. This includes class instances and classes themselves. For example, you can force an input to be an `int`, a number `int | float`, an instance of the self defined `MyClass`, or a even a vector with `list[int]`. Items like `typing.List`, `typing.Dict`, `typing.Union` and `typing.Optional` are supported.
 46
 47You can pass union types to validate one of multiple types. For example, you could validate an input was an int or a float with `int | float` or `typing.Union[int, float]`.
 48
 49Nesting is allowed as long as the nested items are iterables (e.g. `typing.List`, `dict`, ...). For example, you could validate that a list is a vector with `list[int]` or possibly `typing.List[int]`.
 50
 51Variables without an annotation for type are not enforced.
 52
 53## Why use Type Enforced?
 54
 55- `type_enforced` is a pure python type enforcer that does not require any special compiler or preprocessor to work. 
 56- `type_enforced`  uses the standard python typing hints and enforces them at runtime.
 57    - This means that you can use it in any python environment (3.11+) without any special setup.
 58- `type_enforced`  is designed to be lightweight and easy to use, making it a great choice for both small and large projects.
 59- `type_enforced`  supports complex (nested) typing hints, union types, and many of the standard python typing functions.
 60- Other Options:
 61    - `Pydantic`:
 62        - A great all around option.
 63        - Its ~40% slower at scale than type enforced for larger object verifications with complex (nested) types.
 64        - It is ~30% faster for verifications of smaller objects.
 65        - It is designed for data classes
 66        - We have found it to be very consistent with type checking including complex (nested) type hints.
 67    - `Beartype`:
 68        - The fastest option we tested by far (close to O(n) time complexity).
 69        - Is great for simple type checking and we highly recommend it for that use case. 
 70        - Is inconsistent at catching errors with complex (nested) type hints (at least in beartype 0.21.0)
 71            - EG: Running the same data over a loop randomly fails to catch type errors.
 72    - `Typeguard`:
 73        - A useable option.
 74        - ~100% slower than type enforced for simple type checking.
 75        - Does not support complex (nested) type hints.
 76    - `Enforce`:
 77        - Has not been updated since 2017.
 78        - We were unable to get working with python 3.13.
 79- Note: See the [benchmarks](https://github.com/connor-makowski/type_enforced/blob/main/benchmark.md) for more information on the performance of each type checker.
 80
 81## Supported Type Checking Features:
 82
 83- Function/Method Input Typing
 84- Function/Method Return Typing
 85- Dataclass Typing
 86- All standard python types (`str`, `list`, `int`, `dict`, ...)
 87- Union types
 88    - typing.Union
 89    - `|` separated items (e.g. `int | float`)
 90- Nested types (e.g. `dict[str, int]` or `list[int|float]`)
 91    - Note: Each parent level must be an iterable
 92        - Specifically a variant of `list`, `set`, `tuple` or `dict`
 93    - Note: `dict` requires two types to be specified (unions count as a single type)
 94        - The first type is the key type and the second type is the value type
 95        - e.g. `dict[str, int|float]` or `dict[int, float]`
 96    - Note: `list` and `set` require a single type to be specified (unions count as a single type)
 97        - e.g. `list[int]`, `set[str]`, `list[float|str]`
 98    - Note: `tuple` Allows for `N` types to be specified
 99        - Each item refers to the positional type of each item in the tuple
100        - Support for ellipsis (`...`) is supported if you only specify two types and the second is the ellipsis type
101            - e.g. `tuple[int, ...]` or `tuple[int|str, ...]`
102        - Note: Unions between two tuples are not supported
103            - e.g. `tuple[int, str] | tuple[str, int]` will not work
104    - Deeply nested types are supported too:
105        - `dict[dict[int]]`
106        - `list[set[str]]`
107- Many of the `typing` (package) functions and methods including:
108    - Standard typing functions:
109        - `List`
110        - `Set`
111        - `Dict`
112        - `Tuple`
113    - `Union`
114    - `Optional`
115    - `Any`
116    - `Sized`
117        - Essentially creates a union of:
118            - `list`, `tuple`, `dict`, `set`, `str`, `bytes`, `bytearray`, `memoryview`, `range`
119        - Note: Can not have a nested type
120            - Because this does not always meet the criteria for `Nested types` above
121    - `Literal`
122        - Only allow certain values to be passed. Operates slightly differently than other checks.
123        - e.g. `Literal['a', 'b']` will require any passed values that are equal (`==`) to `'a'` or `'b'`.
124            - This compares the value of the passed input and not the type of the passed input.
125        - Note: Multiple types can be passed in the same `Literal` as acceptable values.
126            - e.g. Literal['a', 'b', 1, 2] will require any passed values that are equal (`==`) to `'a'`, `'b'`, `1` or `2`.
127        - Note: If type is a `str | Literal['a', 'b']`
128            - The check will validate that the type is a string or the value is equal to `'a'` or `'b'`.
129            - This means that an input of `'c'` will pass the check since it matches the string type, but an input of `1` will fail.
130        - Note: If type is a `int | Literal['a', 'b']`
131            - The check will validate that the type is an int or the value is equal to `'a'` or `'b'`.
132            - This means that an input of `'c'` will fail the check, but an input of `1` will pass.
133        - Note: Literals stack when used with unions.
134            - e.g. `Literal['a', 'b'] | Literal[1, 2]` will require any passed values that are equal (`==`) to `'a'`, `'b'`, `1` or `2`.
135    - `Callable`
136        - Essentially creates a union of:
137            - `staticmethod`, `classmethod`, `types.FunctionType`, `types.BuiltinFunctionType`, `types.MethodType`, `types.BuiltinMethodType`, `types.GeneratorType`
138    - Note: Other functions might have support, but there are not currently tests to validate them
139        - Feel free to create an issue (or better yet a PR) if you want to add tests/support
140- `Constraint` validation.
141    - This is a special type of validation that allows passed input to be validated.
142        - Standard and custom constraints are supported.
143    - This is useful for validating that a passed input is within a certain range or meets a certain criteria.
144    - Note: Constraints stack when used with unions.
145        - e.g. `int | Constraint(ge=0) | Constraint(le=5)` will require any passed values to be integers that are greater than or equal to `0` and less than or equal to `5`.
146    - Note: The constraint is checked after type checking occurs and operates independently of the type checking.
147        - This operates differently than other checks (like `Literal`) and is evaluated post type checking.
148        - For example, if you have an annotation of `str | Constraint(ge=0)`, this will always raise an exception since if you pass a string, it will raise on the constraint check and if you pass an integer, it will raise on the type check.
149    - Note: See the example below or technical [constraint](https://connor-makowski.github.io/type_enforced/type_enforced/utils.html#Constraint) and [generic constraint](https://connor-makowski.github.io/type_enforced/type_enforced/utils.html#GenericConstraint) docs for more information.
150    ```
151
152## Interactive Example
153
154```py
155>>> import type_enforced
156>>> @type_enforced.Enforcer
157... def my_fn(a: int , b: int|str =2, c: int =3) -> None:
158...     pass
159...
160>>> my_fn(a=1, b=2, c=3)
161>>> my_fn(a=1, b='2', c=3)
162>>> my_fn(a='a', b=2, c=3)
163Traceback (most recent call last):
164  File "<python-input-2>", line 1, in <module>
165    my_fn(a='a', b=2, c=3)
166    ~~~~~^^^^^^^^^^^^^^^^^
167  File "/app/type_enforced/enforcer.py", line 233, in __call__
168    self.__check_type__(assigned_vars.get(key), value, key)
169    ~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
170  File "/app/type_enforced/enforcer.py", line 266, in __check_type__
171    self.__exception__(
172    ~~~~~~~~~~~~~~~~~~^
173        f"Type mismatch for typed variable `{key}`. Expected one of the following `{list(expected.keys())}` but got `{obj_type}` with value `{obj}` instead."
174        ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
175    )
176    ^
177  File "/app/type_enforced/enforcer.py", line 188, in __exception__
178    raise TypeError(f"TypeEnforced Exception ({self.__fn__.__qualname__}): {message}")
179TypeError: TypeEnforced Exception (my_fn): Type mismatch for typed variable `a`. Expected one of the following `[<class 'int'>]` but got `<class 'str'>` with value `a` instead.
180```
181
182## Nested Examples
183```py
184import type_enforced
185import typing
186
187@type_enforced.Enforcer
188def my_fn(
189    a: dict[str,dict[str, int|float]], # Note: For dicts, the key is the first type and the value is the second type
190    b: list[typing.Set[str]] # Could also just use set
191) -> None:
192    return None
193
194my_fn(a={'i':{'j':1}}, b=[{'x'}]) # Success
195
196my_fn(a={'i':{'j':'k'}}, b=[{'x'}]) # Error =>
197# TypeError: TypeEnforced Exception (my_fn): Type mismatch for typed variable `a['i']['j']`. Expected one of the following `[<class 'int'>, <class 'float'>]` but got `<class 'str'>` with value `k` instead.
198```
199
200## Class and Method Use
201
202Type enforcer can be applied to methods individually:
203
204```py
205import type_enforced
206
207class my_class:
208    @type_enforced.Enforcer
209    def my_fn(self, b:int):
210        pass
211```
212
213You can also enforce all typing for all methods in a class by decorating the class itself.
214
215```py
216import type_enforced
217
218@type_enforced.Enforcer
219class my_class:
220    def my_fn(self, b:int):
221        pass
222
223    def my_other_fn(self, a: int, b: int | str):
224      pass
225```
226
227You can also enforce types on `staticmethod`s and `classmethod`s if you are using `python >= 3.10`. If you are using a python version less than this, `classmethod`s and `staticmethod`s methods will not have their types enforced.
228
229```py
230import type_enforced
231
232@type_enforced.Enforcer
233class my_class:
234    @classmethod
235    def my_fn(self, b:int):
236        pass
237
238    @staticmethod
239    def my_other_fn(a: int, b: int | str):
240      pass
241```
242
243Dataclasses are suported too.
244
245```py
246import type_enforced
247from dataclasses import dataclass
248
249@type_enforced.Enforcer
250@dataclass
251class my_class:
252    foo: int
253    bar: str
254```
255
256You can skip enforcement if you add the argument `enabled=False` in the `Enforcer` call.
257- This is useful for a production vs debugging environment.
258- This is also useful for undecorating a single method in a larger wrapped class.
259- Note: You can set `enabled=False` for an entire class or simply disable a specific method in a larger wrapped class.
260- Note: Method level wrapper `enabled` values take precedence over class level wrappers.
261```py
262import type_enforced
263@type_enforced.Enforcer
264class my_class:
265    def my_fn(self, a: int) -> None:
266        pass
267
268    @type_enforced.Enforcer(enabled=False)
269    def my_other_fn(self, a: int) -> None:
270        pass
271```
272
273## Validate with Constraints
274Type enforcer can enforce constraints for passed variables. These constraints are validated after any type checks are made.
275
276To enforce basic input values are integers greater than or equal to zero, you can use the [Constraint](https://connor-makowski.github.io/type_enforced/type_enforced/utils.html#Constraint) class like so:
277```py
278import type_enforced
279from type_enforced.utils import Constraint
280
281@type_enforced.Enforcer()
282def positive_int_test(value: int |Constraint(ge=0)) -> bool:
283    return True
284
285positive_int_test(1) # Passes
286positive_int_test(-1) # Fails
287positive_int_test(1.0) # Fails
288```
289
290To enforce a [GenericConstraint](https://connor-makowski.github.io/type_enforced/type_enforced/utils.html#GenericConstraint):
291```py
292import type_enforced
293from type_enforced.utils import GenericConstraint
294
295CustomConstraint = GenericConstraint(
296    {
297        'in_rgb': lambda x: x in ['red', 'green', 'blue'],
298    }
299)
300
301@type_enforced.Enforcer()
302def rgb_test(value: str | CustomConstraint) -> bool:
303    return True
304
305rgb_test('red') # Passes
306rgb_test('yellow') # Fails
307```
308
309
310
311## Validate class instances and classes
312
313Type enforcer can enforce class instances and classes. There are a few caveats between the two.
314
315To enforce a class instance, simply pass the class itself as a type hint:
316```py
317import type_enforced
318
319class Foo():
320    def __init__(self) -> None:
321        pass
322
323@type_enforced.Enforcer
324class my_class():
325    def __init__(self, object: Foo) -> None:
326        self.object = object
327
328x=my_class(Foo()) # Works great!
329y=my_class(Foo) # Fails!
330```
331
332Notice how an initialized class instance `Foo()` must be passed for the enforcer to not raise an exception.
333
334To enforce an uninitialized class object use `typing.Type[classHere]` on the class to enforce inputs to be an uninitialized class:
335```py
336import type_enforced
337import typing
338
339class Foo():
340    def __init__(self) -> None:
341        pass
342
343@type_enforced.Enforcer
344class my_class():
345    def __init__(self, object_class: typing.Type[Foo]) -> None:
346        self.object = object_class()
347
348y=my_class(Foo) # Works great!
349x=my_class(Foo()) # Fails
350```
351
352By default, type_enforced will check for subclasses of a class when validating types. This means that if you pass a subclass of the expected class, it will pass the type check.
353
354Note: Uninitialized class objects that are passed are not checked for subclasses.
355
356```py
357import type_enforced
358
359class Foo:
360    pass
361
362class Bar(Foo):
363    pass
364
365class Baz:
366    pass
367
368@type_enforced.Enforcer
369def my_fn(custom_class: Foo):
370    pass
371
372my_fn(Foo()) # Passes as expected
373my_fn(Bar()) # Passes as expected
374my_fn(Baz()) # Raises TypeError as expected
375```
376
377## What changed in 2.0.0?
378The main changes in version 2.0.0 revolve around migrating towards the standard python typing hint process and away from the original type_enfoced type hints (as type enforced was originally created before the `|` operator was added to python).
379- Support for python3.10 has been dropped.
380- List based union types are no longer supported.
381    - For example `[int, float]` is no longer a supported type hint.
382    - Use `int|float` or `typing.Union[int, float]` instead.
383- Dict types now require two types to be specified.
384    - The first type is the key type and the second type is the value type.
385    - For example, `dict[str, int|float]` or `dict[int, float]` are valid types.
386- Tuple types now allow for `N` types to be specified.
387    - Each item refers to the positional type of each item in the tuple.
388    - Support for ellipsis (`...`) is supported if you only specify two types and the second is the ellipsis type.
389        - For example, `tuple[int, ...]` or `tuple[int|str, ...]` are valid types.
390    - Note: Unions between two tuples are not supported.
391        - For example, `tuple[int, str] | tuple[str, int]` will not work.
392- Constraints and Literals can now be stacked with unions.
393    - For example, `int | Constraint(ge=0) | Constraint(le=5)` will require any passed values to be integers that are greater than or equal to `0` and less than or equal to `5`.
394    - For example, `Literal['a', 'b'] | Literal[1, 2]` will require any passed values that are equal (`==`) to `'a'`, `'b'`, `1` or `2`.
395- Literals now evaluate during the same time as type checking and operate as OR checks.
396    - For example, `int | Literal['a', 'b']` will validate that the type is an int or the value is equal to `'a'` or `'b'`.
397- Constraints are still are evaluated after type checking and operate independently of the type checking.
398
399# Development
400## Running Tests, Prettifying Code, and Updating Docs
401
402Make sure Docker is installed and running.
403
404- Create a docker container and drop into a shell
405    - `./run.sh`
406- Run all tests (see ./utils/test.sh)
407    - `./run.sh test`
408- Prettify the code (see ./utils/prettify.sh)
409    - `./run.sh prettify`
410- Update the docs (see ./utils/docs.sh)
411    - `./run.sh docs`
412
413- Note: You can and should modify the `Dockerfile` to test different python versions."""
414from .enforcer import Enforcer, FunctionMethodEnforcer