Welcome to the Comprehensive Guide to Python’s `accepts` API

In this detailed guide, we introduce the `accepts` decorator in Python, which is used for type checking of function arguments. We’ll provide several API explanations, complete with code snippets, and an example application utilizing these APIs.

Introduction to `accepts`

The `accepts` decorator in Python is a useful tool for enforcing type checks on function arguments. It helps to ensure that the correct types are being passed to functions, which can prevent runtime errors and improve code readability.

Example Usage

from typing import Any, Tuple

def accepts(*types: Tuple[Any]) -> callable:
    def check_accepts(f: callable) -> callable:
        def new_f(*args, **kwargs):
            if len(args) != len(types):
                raise ValueError(f'Expected {len(types)} arguments but got {len(args)}.')
            for (a, t) in zip(args, types):
                if not isinstance(a, t):
                    raise TypeError(f'Argument {a} does not match {t}.')
            return f(*args, **kwargs)
        return new_f
    return check_accepts

@accepts(int, int)
def add(x: int, y: int) -> int:
    return x + y

print(add(10, 20))  # Output: 30
# print(add(10, '20'))  # Raises TypeError

API Explanations

The `accepts` function can be customized and extended for various applications. Here are some expanded examples:

from typing import Any, Tuple, Dict

def accepts(*types: Tuple[Any]) -> callable:
    def check_accepts(f: callable) -> callable:
        def new_f(*args, **kwargs):
            if len(args) != len(types):
                raise ValueError(f'Expected {len(types)} arguments but got {len(args)}.')
            for (a, t) in zip(args, types):
                if not isinstance(a, t):
                    raise TypeError(f'Argument {a} does not match {t}.')
            return f(*args, **kwargs)
        return new_f
    return check_accepts

@accepts(int, int)
def multiply(x: int, y: int) -> int:
    return x * y

print(multiply(10, 20))  # Output: 200

@accepts(str, str)
def concatenate(a: str, b: str) -> str:
    return a + b

print(concatenate('hello', ' world'))  # Output: 'hello world'

Complete Application Example

Below is a complete example of a hypothetical application using the `accepts` decorator:

from typing import List

def accepts(*types) -> callable:
    def check_accepts(f: callable) -> callable:
        def new_f(*args, **kwargs):
            if len(args) != len(types):
                raise ValueError(f'Expected {len(types)} arguments but got {len(args)}.')
            for (a, t) in zip(args, types):
                if not isinstance(a, t):
                    raise TypeError(f'Argument {a} does not match {t}.')
            return f(*args, **kwargs)
        return new_f
    return check_accepts

@accepts(str, int, List[int])
def process_data(name: str, count: int, values: List[int]) -> str:
    total = sum(values)
    return f'{name} has {count} items with a total value of {total}'

print(process_data('Alice', 3, [10, 20, 30]))  # Output: 'Alice has 3 items with a total value of 60'

Such custom decorators can significantly enhance the robustness and maintainability of your code by ensuring strict type adherence. This guide with comprehensive code snippets allows you to quickly integrate and make use of the `accepts` decorator in your applications.

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