枚举 - 选择

要定义一个可以从预定义值集中获取值的CLI 参数，可以使用标准 Python enum.Enum

from enum import Enum

import typer


class NeuralNetwork(str, Enum):
    simple = "simple"
    conv = "conv"
    lstm = "lstm"


def main(network: NeuralNetwork = NeuralNetwork.simple):
    print(f"Training neural network of type: {network.value}")


if __name__ == "__main__":
    typer.run(main)

提示

请注意，函数参数network将是Enum，而不是str。

要在函数代码中获取str值，请使用network.value。

检查它

$ python main.py --help

// Notice the predefined values [simple|conv|lstm]
Usage: main.py [OPTIONS]

Options:
  --network [simple|conv|lstm]  [default: simple]
  --help                        Show this message and exit.

// Try it
$ python main.py --network conv

Training neural network of type: conv

// Invalid value
$ python main.py --network capsule

Usage: main.py [OPTIONS]
Try "main.py --help" for help.

Error: Invalid value for '--network': 'capsule' is not one of 'simple', 'conv', 'lstm'.

// Note that enums are case sensitive by default
$ python main.py --network CONV

Usage: main.py [OPTIONS]
Try "main.py --help" for help.

Error: Invalid value for '--network': 'CONV' is not one of 'simple', 'conv', 'lstm'.

不区分大小写的枚举选择

您可以使用case_sensitive参数使Enum（选择）CLI 参数不区分大小写

Python 3.7+Python 3.7+ 非注释

from enum import Enum

import typer
from typing_extensions import Annotated


class NeuralNetwork(str, Enum):
    simple = "simple"
    conv = "conv"
    lstm = "lstm"


def main(
    network: Annotated[
        NeuralNetwork, typer.Option(case_sensitive=False)
    ] = NeuralNetwork.simple,
):
    print(f"Training neural network of type: {network.value}")


if __name__ == "__main__":
    typer.run(main)

提示

如果可能，建议使用Annotated版本。

from enum import Enum

import typer


class NeuralNetwork(str, Enum):
    simple = "simple"
    conv = "conv"
    lstm = "lstm"


def main(
    network: NeuralNetwork = typer.Option(NeuralNetwork.simple, case_sensitive=False),
):
    print(f"Training neural network of type: {network.value}")


if __name__ == "__main__":
    typer.run(main)

然后，无论使用小写、大写还是混合大小写，都会检查Enum的值

// Notice the upper case CONV
$ python main.py --network CONV

Training neural network of type: conv

// A mix also works
$ python main.py --network LsTm

Training neural network of type: lstm

枚举值的列表

一个CLI 参数也可以接受一个Enum值的列表。

Python 3.7+Python 3.7+ 非注解

from enum import Enum
from typing import List

import typer
from typing_extensions import Annotated


class Food(str, Enum):
    food_1 = "Eggs"
    food_2 = "Bacon"
    food_3 = "Cheese"


def main(groceries: Annotated[List[Food], typer.Option()] = [Food.food_1, Food.food_3]):
    print(f"Buying groceries: {', '.join([f.value for f in groceries])}")


if __name__ == "__main__":
    typer.run(main)

提示

如果可能，建议使用Annotated版本。

from enum import Enum
from typing import List

import typer


class Food(str, Enum):
    food_1 = "Eggs"
    food_2 = "Bacon"
    food_3 = "Cheese"


def main(groceries: List[Food] = typer.Option([Food.food_1, Food.food_3])):
    print(f"Buying groceries: {', '.join([f.value for f in groceries])}")


if __name__ == "__main__":
    typer.run(main)

这与任何其他接受事物列表的参数值的工作方式相同。

$ python main.py --help

// Notice the default values being shown
Usage: main.py [OPTIONS]

Options:
  --groceries [Eggs|Bacon|Cheese]  [default: Eggs, Cheese]
  --help                           Show this message and exit.

// Try it with the default values
$ python main.py

Buying groceries: Eggs, Cheese

// Try it with a single value
$ python main.py --groceries "Eggs"

Buying groceries: Eggs

// Try it with multiple values
$ python main.py --groceries "Eggs" --groceries "Bacon"

Buying groceries: Eggs, Bacon