wirinj 0.7.5

A comfy dependency injection library.

Wirinj

A comfy dependency injection library for Python 3.

Why choose wirinj

• Minimal boiler plate code.
• Injection via __init__ or via attributes.
• Dependencies automatically detected through reflection.
• No naming conventions required.
• Zero code factories.
• Friendly with IDE 's code completion (e.g. with PyCharm).
• Autowiring option.
• Injection reports to easily debug dependency problems.
• Simple but powerful wiring configuration.
• Open and extendable architecture.

Installation

Python >= 3.6

Tested with Python 3.6, 3.7 and 3.8.

$ pip install wirinj

Table of Contents

• How to use it
• Code completion on the IDE
• Injection types
  • Into attributes
  • Into init arguments
• Factories
• Dependency definitions
  • Definition format
  • dict keys
  • dict values
  • When to specify the class
  • Creation-context-dependent definition
  • Custom-built dependencies
  • Custom-built dependencies with arguments
  • Split definitions
• Autowiring
  • Heuristic rules
  • Autowiring for production
  • Autowiring report
  • No singletons option
• Injection reports
  • Debugging the injection
  • Missing dependencies
  • Instance error
• A complete injection example

How to use it

Example (autowiring.py):

...

class MyService:
    ...

class MyObject:
    my_service: MyService = INJECTED
    my_config: str = INJECTED

    def __init__(self, param):
        ...


config = {
    'my_config': 'some conf',
}


# Use a function to get access to the root dependencies
@inject(Definitions(config), Autowiring())
def do(
        my_service: MyService,
        my_object_factory: Type[MyObject]
):
    print(my_service)

    my_object1 = my_object_factory(10)
    print(my_object1)


# Inject and run it
do()

Output:

my_service = <MyService>
my_object1 = <MyObject> -> my_config: "some conf", param: 10, my_service: <MyService>

Explanation of the example above

MyService and MyObject are the two user classes.

Attributes my_service and my_config are set with the constant INJECTED to indicate that they must be injected.

The function named do, or any other name you choose, will contain the code inside the injection context with access to any required dependency. This function is decorated with @inject which will inject the dependencies into the function parameters. @inject takes as arguments one or more dependency sources. In this case, a Definition for the static config values and an Autowiring to automatically instantiate the required objects. wirinj will use this ordered list of sources to locate any required dependency.

As the first parameter in the function do (my_service) is not defined in the first source (config), the dependency will be requested to the second one (Autowiring) which will lazily instantiate a MyService object. The class is inferred from the type annotation of the parameter. By default Autowiring will make the object a singleton and therefore any subsequent request for this class will get the same unique instance.

The second parameter (my_object_factory) is annotated as Type[MyObject]. Type[] is part of the standard typing library. It indicates that the parameter is not expected to contain an object of class MyObject but the class MyObject itself or a subclass of it. Therefore you may use my_object_factory variable the same way you would use MyObject class. In the body of the function do, this parameter is called to instantiate a new MyObject. By calling my_object_factory instead of MyObject you make wirinj to inject all its required dependencies during the instantiation:

# This instantiate a MyObject but, as expected, nothing is injected.
obj = MyObject(10)

# This is the same but its dependencies are automatically injected BEFORE running __init__ 
obj = my_object_factory(10)

The last line runs the function do with no parameters. The @inject decorator will inject them from the dependency sources.

Code completion on the IDE

If you are using an IDE such as PyCharm, you will notice that code completion will work as expected in the previous example even for the factory.

Injection types

Into attributes

Example (attribute_injection.py):

class Cat:
    feeder: Feeder = INJECTED

    def __init__(self, color, weight):
        ...

@inject(...)
def fn(factory: Type[Cat]):
    cat = factory('blue', 12)

When you call the factory:

1. The attributes set to INJECTED are located and injected.
2. The __init__ method is called.

Into __init__ arguments

Example (init_injection.py):

class Cat:
    def __init__(self, color, weight, feeder: Feeder = INJECTED):
        ...

@inject(...)
def fn(factory: Type[Cat]):
    cat = factory('blue', 12)
...

• color and weight are passed to __init__.
• feeder is injected.

For __init__ injections, it is not required to set INJECTED as a default value. Any missing argument not passed to the constructor will be injected too. This way you can inject into third party classes whose code is not under your control. However, if you can assign INJECTED as a default value, the IDE won't complain about a missing argument and at the same time you will get a MissingDependenciesError if the dependency is missing which is helpful to early detect dependency issues.

Factories

I have already explained in the first example of this README how factories are used. Now, I'm going to elaborate on that a little more.

You can think of a Python class as an object factory because if you call it as a function, you get a new instance of the object. Wirinj allows you to inject classes so that they can be used as factories. Unlike the original class, the injected version will also inject dependencies into the newly created objects.

Pay attention to the function func and its argument cat_class in this example:

from typing import Type

class Cat:
    pass

class BlackCat(Cat):
    pass

def func(cat_class: Type[Cat]):
    return cat_class()

print('Cat: ', func(Cat))
print('BlackCat: ', func(BlackCat))

Output:

Cat:  <__main__.Cat object at 0x7fc458bc7588>
BlackCat:  <__main__.BlackCat object at 0x7fc458bc7588>

Note that you pass a class and not an object as an argument.

Type[Cat], as described in the typing library docs, represents the class Cat or a subclass of it. A parameter annotated with Type[] expects to receive a class and not an object. In the body of the function, the parameter can be used the same way as its subscribed class would be used.

Example (factory.py):

from typing import Type

class Cat:
    def __init__(self, sound):
        ...

@inject(...)
def fn(cat_factory: Type[Cat]):
    cat = cat_factory('Meow')
    print('cat:', cat)

fn()

By using the Type[] annotation, the IDE recognizes the parameter as if it was the original class but with the difference that any newly created object will be automatically injected.

# This instantiate a Cat object but, as expected, nothing is injected.
cat = Cat('Meow')

# This is the same but its dependencies are automatically injected BEFORE running __init__ 
cat = cat_factory('Meow')

You have both things: the injection is enabled and the IDE completion is fully functional:

Dependency definitions

In previous examples, the wiring configuration has been relegated to the automatic Autowiring class. However you will gain control by explicity defining how your dependencies have to be met. The general way to do this would be something like this:

defs = {
    Cat: Instance(),
    'dog': Singleton(Dog),
}

@inject(Definitions(defs))

The Definitions class allows you to configure the wiring of your classes according to one or more definition dict parameters (defs in the example). The first definitions take precedence.

Definition format

One or several dict arguments passed to the Definition class define your dependency configuration. Each key represents an argument being injected. The value represents how is it injected:

dict keys

• If the key is a class, it will match the argument's type annotation. E.g.: the first key in the example above causes any argument of type Cat, no matter its name, to be injected with a new instance of Cat.

• If the key is a string it will match the argument name. E.g.: the second key causes any argument with name 'dog' to be injected with a unique Dog instance. Here, as the class can't be inferred from the key, you need to explicitly provide the class as an argument: 'dog': Singleton(Dog).

dict values

Each value in the dict can be:

• A literal value you want to be injected. E.g. 'db_name': 'my-db'.
• Instance: inject a new instance each time.
• Singleton: inject the same unique instance every time.
• Factory: inject a factory object that can be called to create new injected objects dynamically.
• CustomInstance: similar to Instance but you provide a custom function to have full control over instantiation.
• CustomSingleton: similar to Singleton but you provide a custom function which will create the object.
• Any other user defined subclasses of DependencyBuilder or Dependency.

Example (definition_types.py):

class House:
    pass


class Cat:
    pass


class Dog:
    pass


def dog_builder():
    """ Custom instantiation """
    dog = Dog()
    dog.random = randint(50, 100)
    return dog


defs = {
    House: Singleton(),
    Cat: Instance(),
    Type[Cat]: Factory(),
    Dog: CustomInstance(dog_builder),
    Type[Dog]: Factory(),
}


@inject(Definitions(defs))
def fn(house: House, cat_factory: Type[Cat], dog_factory: Type[Dog]):
    cat = cat_factory()
    dog = dog_factory()

    print('house:', house)
    print('cat:', cat)
    print('dog:', dog)


fn()

When to specify the class

Instance, Singleton and Factory accept an optional class argument to indicate the class of the object being created. There are two use cases where you need to pass the class:

• The key is a string and therefore the dependency class is undefined.
• The attribute or argument being injected is annotated with a base class but you want to provide a specific subclass of it.

Example of both use cases (explicit_class.py):

class Pet:
    pass

class Cat(Pet):
    pass

class Dog(Pet):
    pass

defs = {
    'cat': Singleton(Cat),
    Pet: Singleton(Dog),
}

@inject(Definitions(defs))
def fn(cat, pet: Pet):
    print('cat is a', cat.__class__.__name__)
    print('pet is a', pet.__class__.__name__)    
fn()

Output:

cat is a Cat
pet is a Dog

Creation-context-dependent definition

class Nail:
    pass

class Leg:
    def __init__(self, nail: Nail):
        pass

class Cat:
    def __init__(self, leg: Leg):
        pass

Imagine wirinj is injecting a Cat which requires a Leg which requires a Nail. The injector will gather:

• First, the Nail that has no dependencies.
• Then, the Leg with the Nail as an argument.
• Finally, the Cat with the Leg as an argument.

We can think of this process as a path: Cat -> nail:Nail -> leg:Leg. I call this the instantiation path.

You can explicitly specify a instantiation path constraint in the definition dict.

Example (instantiation_path.py):

class Animal:
    def __init__(self, sound):
        self.sound = sound

class Dog(Animal):
    pass

class Cat(Animal):
    pass

class Cow(Animal):
    pass

defs = {
    Dog: Instance(),
    Cat: Instance(),
    Cow: Instance(),

    (Dog, 'sound'): 'woof',
    (Cat, 'sound'): 'meow',
    'sound': '?',
}

@inject(Definitions(defs))
def fn(cat: Cat, dog: Dog, cow: Cow):
    print('Cat:', cat.sound)
    print('Dog:', dog.sound)
    print('Cow:', cow.sound)

fn()

Output:

Cat: meow
Dog: woof
Cow: ?

To restrict a definition entry to a particular instantiation path we use a tuple in the key part. This tuple must match the last entries in the instantiation path.

For each tuple entry, a string refers to the argument name and a class refers to the argument type annotation.

If two entries match the required dependency, the more specific one will be chosen.

Custom-built dependencies

Instance and Singleton are used for simple class instantiation. When a custom process is required to create or locate the dependency, use CustomInstance or CustomSingleton. Both take a function as an argument.

Example (custom_build.py):

from random import randint

from wirinj import CustomInstance, inject, Definitions


class Cat:
    def __init__(self, color, weight):
        self.color = color
        self.weight = weight

    def __str__(self):
        return f'A {self.color} pounds {self.weight} cat.'


def create_cat(color):
    return Cat(color, randint(4, 20))


defs = {
    'color': 'blue',
    Cat: CustomInstance(create_cat),
}


@inject(Definitions(defs))
def fn(cat1: Cat, cat2: Cat, cat3: Cat):
    print(cat1)
    print(cat2)
    print(cat3)


fn()

Output:

A 11 pounds blue cat.
A 5 pounds blue cat.
A 14 pounds blue cat.

Custom-built dependencies with arguments

In the previous example, the object is instantiated without arguments, so all of its __init__ arguments are injected from dependencies.

If your constructor requires some arguments to be passed (explicit arguments) and others to be injected (injection arguments), I recommend to follow these rules:

1. In the __init__ method, put the explicit arguments first and then the injection arguments. This allow you to use positional arguments when you create the object.

2. Set the default value of the injection arguments to INJECTED. This way the IDE code completion will not complain about missing arguments. Also, this is the only way you can have defaults in your explicit arguments when they are followed by injection arguments.

3. About the builder function that you pass to CustomInstance, use the same name and position for the explicit arguments as you use in the __init__ method. The rest of the arguments don't have to be related at all to the __init__ arguments. Indeed, you can specify as many dependency arguments as you need to create the object. The injection process will inspect the function signature and will provide them.

Example (custom_build_with_args.py):

from random import randint
from typing import Type

from wirinj import CustomInstance, Factory, inject, Definitions


class Cat:
    def __init__(self, name, color=None, weight=None):
        self.name = name
        self.color = color
        self.weight = weight

    def __str__(self):
        return f'{self.name} is a {self.color} pounds {self.weight} cat.'


def create_cat(name, color):
    return Cat(name, color, randint(4, 20))


defs = {
    'color': 'black',
    Cat: CustomInstance(create_cat),
    Type[Cat]: Factory(),
}


@inject(Definitions(defs))
def fn(factory: Type[Cat]):
    cat = factory('Tom')
    print(cat)
    cat2 = factory('Sam')
    print(cat2)


fn()

Output:

Tom is a 8 pounds black cat.
Sam is a 14 pounds black cat.

About the 3 arguments of Cat.__init__:

• One comes from calling the factory.
• Another one from the dependency configuration.
• The third is generated by the custom creation function.

Split definitions

You can split the dependency configuration in several dict definitions.

Example (split_definitions.py):

class Cat:
    sound: str = INJECTED
    weight: float = INJECTED

config = {
    'sound': 'meow',
    'weight': 5,
}

wiring = {
    Cat: Instance(),
    Type[Cat]: Factory()
}

@inject(Definitions(config, wiring))
def fn(...):
    ...

Definitions accepts any number of definition dicts.

Autowiring

You can add an Autowiring instance as a last resort to provide a dependency when it is undefined. The automatically created dependency will be of type Instance, Singleton or Factory dependening on the context.

Example (autowiring.py):

class MyService:
    def __str__(self):
        return '<MyService>'


class MyObject:
    my_service: MyService = INJECTED
    my_config: str = INJECTED
    ...    

    def __init__(self, param):
        self.param = param


config = {
    'my_config': 'some conf',
}


# Use a function to get access to the root dependencies
@inject(Definitions(config), Autowiring())
def do(
        my_service: MyService,
        my_object_factory: Type[MyObject]
):
    print(my_service)

    my_object1 = my_object_factory(10)
    print(my_object1)


# Inject and run it
do()

Output:

<MyService>
<MyObject> -> my_config: "some conf", param: 10, my_service: <MyService>

my_config is the only dependency explicitly defined. The 3 others fall back to Autowiring which will automatically create:

• A MyService singleton.
• A Type[MyObject] factory.
• A MyObject instance when the factory is called.

Heuristic rules

Autowiring works only for arguments that have a type annotation:

• If the annotation is a class, as with dog: Dog in the previous example, a singleton will be generated.
• If it is Type[class], as with horse_factory: Type[Horse], a factory will be provided.
• If the injection comes from a factory, as when horse_factory() is called, an instance will be created.

Autowiring for production

In my opinion, this kind of magic should not be used in production environments; you should not take the risk of leaving such important wiring decisions in the hands of an heuristic algorithm.

Fortunately, you can use AutowiringReport class to easily convert the autowiring configuration into a regular dependency definition:

Autowiring report

It's quite simple to use; just pass an AutowiringReport instance to Autowiring:

Example (autowiring_report.py):

report = AutowiringReport()

@inject(Definitions(deps), Autowiring(report))
def fn(cat: Cat, dog: Dog, horse_factory: Type[Horse]):
    ...    

fn()

print(report.get())

Output:

...
--------------- wirinj ---------------
Autowiring report:

Definitions({
    Dog: Singleton(),
    Type[Horse]: Factory(),
    Horse: Instance(),
}),
--------------------------------------

Call report.get() to get the report. Review and copy the definitions to your configuration file, remove Autowiring, and you will be production ready.

No singletons option

You may set use_singletons to False to force all dependencies to be injected as an Instance.

Autowiring(use_singletons=False)

Injection reports

During each injection process, a dependency tree is built with all the dependencies that are being gathered.

As you change your code, your dependency configuration can get out of sync. wirinj include reporting features that can help you to solve this dependency issues:

Debugging the injection

The injection process can be debugged to expose the creation order and the dependency tree.

Take this composition of classes (cat_example_classes.py) :

class Nail:
    pass

class Leg:
    def __init__(self, nail1: Nail, nail2: Nail, nail3: Nail, nail4: Nail, nail5: Nail):
        pass

class Mouth:
    pass

class Ear:
    pass

class Eye:
    pass

class Head:
    def __init__(self, mouth: Mouth, ear1: Ear, ear2: Ear, eye1: Eye, eye2: Eye):
        pass

class Body:
    pass

class Tail:
    pass

class Cat:
    def __init__(self, head: Head, body: Body, tail: Tail, leg1: Leg, leg2: Leg, leg3: Leg, leg4: Leg):
        pass

We can debug the injection process just by setting the logging level to DEBUG and then, requesting a Cat from the Injector:

Example (injection_debug_report.py):

import logging

logging.basicConfig(level=logging.DEBUG, format='%(message)s')

inj = Injector(Autowiring(use_singletons=False))

cat = inj.get(Cat)

Note that we are replacing all the dependency definitions by a simple Autowiring(). We pass the argument use_singletons=False to force all dependencies to be injected as an Instance. By default Autowiring generates Singleton dependencies and, in this case, we don't want all the legs of the Cat to be the same.

The code above returns this:

--------------- wirinj ---------------
        mouth:Mouth
        ear1:Ear
        ear2:Ear
        eye1:Eye
        eye2:Eye
    head:Head
    body:Body
    tail:Tail
        nail1:Nail
        nail2:Nail
        nail3:Nail
        nail4:Nail
        nail5:Nail
    leg1:Leg
        nail1:Nail
        nail2:Nail
        nail3:Nail
        nail4:Nail
        nail5:Nail
    leg2:Leg
        nail1:Nail
        nail2:Nail
        nail3:Nail
        nail4:Nail
        nail5:Nail
    leg3:Leg
        nail1:Nail
        nail2:Nail
        nail3:Nail
        nail4:Nail
        nail5:Nail
    leg4:Leg
:Cat
--------------------------------------

You can see how all the dependencies are gathered, and in which order. The final object is the requested Cat object.

Missing dependencies

Injection doesn't stop when a dependency is missing. It continues building the dependency tree as far as it can. This makes it possible to fix several dependency issues in one shot.

If one ore more dependencies are missing, an ERROR level report will be logged. Therefore, you don't need to change the logging level to get it; just look above the error traceback after a dependency exception.

Example (missing_dependencies_report.py):

    from examples.report.cat_example_classes import Cat, Head
    from wirinj import Injector, Definitions, Instance

    inj = Injector(Definitions({
        Cat: Instance(),
        Head: Instance(),
    }))
    cat2 = inj.get(Cat)

In the example above, we only define the wiring for Cat and Head. All the other dependencies, such as Mouth, Ear, Eye, etc, are undefined.

After running the example, we get:

--------------- wirinj ---------------
Missing dependencies:
        mouth:Mouth *** NotFound ***
        ear1:Ear *** NotFound ***
        ear2:Ear *** NotFound ***
        eye1:Eye *** NotFound ***
        eye2:Eye *** NotFound ***
    head:Head
    body:Body *** NotFound ***
    tail:Tail *** NotFound ***
    leg1:Leg *** NotFound ***
    leg2:Leg *** NotFound ***
    leg3:Leg *** NotFound ***
    leg4:Leg *** NotFound ***
:Cat
--------------------------------------
Traceback (most recent call last):
...
wirinj.errors.MissingDependenciesError: Missing dependencies.

Notice that, although the first dependency, Mouth, failed to be satisfied, the injection process continues in order to gather as much information as possible about the missing dependencies.

With this report, it becomes clear which classes are undefined, and what needs to be added in the injection configuration.

Instance error

If an exception is raised during the instantiation of any of the dependencies, you will not get the dependency tree logs as it happens when a dependency is missing.

You'll need to track the stack trace to fix the problem. However, there is a task planned in the TO-DO list to log the dependency tree in these cases too.

A complete example

This silly example aims to illustrate several aspects of the wirinj library.

The two main classes, Bob and Mike, extend PetDeliveryPerson. They deliver pets to the client using one or more vehicles. Whenever a Vehicle is needed, it is built in advance.

While Bob uses his only vehicle by repeating the route several times, Mike builds a fleet of autonomous vehicles to deliver all the pets in one trip.

The classes (pet_delivery/classes.py):

class Pet:
    def __deps__(self, sound: str, weight):
        self.sound = sound
        self.weight = weight

    @deps
    def __init__(self, gift_wrapped):
        self.gift_wrapped = gift_wrapped

    def cry(self):
        return self.sound.lower() if self.gift_wrapped else self.sound.upper()


class Cat(Pet):
    pass


class Dog(Pet):
    pass


class Bird(Pet):
    pass


class Part:
    def __deps__(self, mount_sound):
        self.mount_sound = mount_sound

    @deps
    def __init__(self):
        pass

    def mount(self):
        return self.mount_sound


class Engine(Part):
    pass


class Plate(Part):
    pass


class Wheel(Part):
    pass


class Container(Part):
    pass


class VehicleBuilder:

    def __deps__(self,
                 engine_factory: Type[Engine],
                 plate_factory: Type[Plate],
                 wheel_factory: Type[Wheel],
                 container_factory: Type[Container],
                 ):
        self.engine_factory = engine_factory
        self.plate_factory = plate_factory
        self.wheel_factory = wheel_factory
        self.container_factory = container_factory

    @deps
    def __init__(self):
        pass

    def build(self, recipe: Dict):
        parts = []  # type: List[Part]
        parts += [self.engine_factory() for _ in range(recipe.get('engines', 0))]
        parts += [self.plate_factory() for _ in range(recipe.get('plates', 0))]
        parts += [self.wheel_factory() for _ in range(recipe.get('wheels', 0))]
        parts += [self.container_factory() for _ in range(recipe.get('containers', 0))]

        mounting = ''
        for part in parts:
            mounting += ' ' + part.mount()

        return mounting


class Vehicle:
    def __deps__(self, builder: VehicleBuilder, recipe: Dict, max_load_weight):
        self.builder = builder
        self.recipe = recipe
        self.max_load_weight = max_load_weight

    @deps
    def __init__(self):
        self.pets = []
        self.build()

    def go(self, miles):
        logger.info('{} goes {} miles'.format(self.__class__.__name__, miles))

    def come_back(self):
        logger.info('{} commes back'.format(self.__class__.__name__))

    def build(self):
        logger.info('{} is built: {}'.format(
            self.__class__.__name__,
            self.builder.build(self.recipe)
        ))

    def get_available_load(self):
        return self.max_load_weight - sum(pet.weight for pet in self.pets)


class Car(Vehicle):
    pass


class Van(Vehicle):
    pass


class Truck(Vehicle):
    pass


class PetLoader:

    def upload(self, pets: List[Pet], vehicle: Vehicle):
        info = 'Uploading to the {}:'.format(vehicle.__class__.__name__.lower())
        while pets:
            pet = pets.pop()
            if vehicle.get_available_load() >= pet.weight:
                vehicle.pets.append(pet)
                info += ' ' + pet.__class__.__name__
            else:
                pets.append(pet)
                break
        logger.info(info)

    def download(self, vehicle):
        logger.info('{} pets delivered'.format(len(vehicle.pets)))
        vehicle.pets = []


class PetPicker:

    def __deps__(self, pet_store: Type[Pet]):
        self.pet_store = pet_store

    @deps
    def __init__(self):
        # raise Exception('HORROR!!!!')
        pass

    def pick(self, qty, gift_wrapped):
        info = 'Picking pets up: '
        pets = []
        for _ in range(qty):
            pet = self.pet_store(gift_wrapped)
            info += ' ' + pet.cry()
            pets.append(pet)
        logger.info(info)
        return pets


class PetDeliveryPerson:

    @deps
    def __init__(self):
        pass

    def deliver(self, pet_qty, miles, gift_wrapped):
        pass


class Bob(PetDeliveryPerson):
    """Bob builds a car and deliver pets in his vehicle repeating the route several times."""

    def __deps__(self, vehicle: Vehicle, pet_picker: PetPicker, pet_loader: PetLoader):
        self.vehicle = vehicle
        self.pet_picker = pet_picker
        self.pet_loader = pet_loader

    def deliver(self, pet_qty, miles, gift_wrapped):
        # Pick up pets
        pets = self.pet_picker.pick(pet_qty, gift_wrapped)

        # Bob owns one vehicle only
        while pets:
            self.pet_loader.upload(pets, self.vehicle)
            self.vehicle.go(miles)
            self.pet_loader.download(self.vehicle)
            self.vehicle.come_back()


class Mike(PetDeliveryPerson):
    """Mike builds several autonomous vehicles and use them to deliver the pets all together"""

    def __deps__(self, vehicle_factory: Type[Vehicle], pet_picker: PetPicker, pet_loader: PetLoader):
        self.vehicle_factory = vehicle_factory
        self.pet_picker = pet_picker
        self.pet_loader = pet_loader

    @deps
    def __init__(self):
        super().__init__()
        self.vehicles = []  # type: List[Vehicle]

    def get_vehicle(self):
        if self.vehicles:
            return self.vehicles.pop()
        else:
            return self.vehicle_factory()

    def park_vehicles(self, vehicles):
        self.vehicles += vehicles

    def deliver(self, pet_qty, miles, gift_wrapped):

        # Pick up pets
        pets = self.pet_picker.pick(pet_qty, gift_wrapped)

        # Get vehicles and upload them
        vehicles = []
        while pets:
            vehicle = self.get_vehicle()
            vehicles.append(vehicle)
            self.pet_loader.upload(pets, vehicle)

        # Go
        for vehicle in vehicles:
            vehicle.go(miles)

        # Deliver pets
        for vehicle in vehicles:
            self.pet_loader.download(vehicle)

        # Come back
        for vehicle in vehicles:
            vehicle.come_back()

        # Park
        self.park_vehicles(vehicles)

Injection definitions (pet_delivery/defs.py):

pet_defs = {
    Dog: Instance(),
    Cat: Instance(),
    Bird: Instance(),

    (Dog, 'sound'): 'Woof',
    (Dog, 'weight'): 10,

    (Cat, 'sound'): 'Meow',
    (Cat, 'weight'): 5,

    (Bird, 'sound'): 'Chirp',
    (Bird, 'weight'): 0.1,
}


vehicle_defs = {
    Engine: Instance(),
    Plate: Instance(),
    Wheel: Instance(),
    Container: Instance(),

    Type[Engine]: Factory(),
    Type[Plate]: Factory(),
    Type[Wheel]: Factory(),
    Type[Container]: Factory(),

    VehicleBuilder: Singleton(),

    (Engine, 'mount_sound'): 'RRRRoarrr',
    (Plate, 'mount_sound'): 'plaf',
    (Wheel, 'mount_sound'): 'pffff',
    (Container, 'mount_sound'): 'BLOOOOM',

    Car: Instance(),
    (Car, 'max_load_weight'): 10,
    (Car, 'recipe'): {
        'engines': 1,
        'plates': 6,
        'wheels': 4,
    },

    Van: Instance(),
    (Van, 'max_load_weight'): 50,
    (Van, 'recipe'): {
        'engines': 1,
        'plates': 8,
        'wheels': 4,
    },

    Truck: Instance(),
    (Truck, 'max_load_weight'): 200,
    (Truck, 'recipe'): {
        'engines': 1,
        'plates': 20,
        'wheels': 12,
        'container': 1,
    },
}

common_defs = {
    PetPicker: Singleton(),
    PetLoader: Singleton(),

    Bob: Singleton(),
    Mike: Singleton(),
}

Running the app (pet_delivery/example_1.py):

world_one_defs = {
    (Bob, Vehicle): Singleton(Car),
    (Bob, PetPicker, Type[Pet]): Factory(Bird),

    (Mike, Type[Vehicle]): Factory(Van),
    (Mike, PetPicker, Type[Pet]): Factory(Cat),
}

world_one = Definitions(
    pet_defs,
    vehicle_defs,
    common_defs,
    world_one_defs,
)

logging.basicConfig(format='%(message)s', level=logging.INFO)

@inject(world_one)
def do(bob: Bob, mike: Mike):
    bob.deliver(100, 5, False)
    bob.deliver(50, 200, True)

    mike.deliver(20, 1000, True)

do()

Running the same app with another wiring configuration (pet_delivery/example_2.py):

world_two_defs = {
    (Bob, Vehicle): Singleton(Van),
    (Bob, PetPicker, Type[Pet]): Factory(Cat),

    (Mike, Type[Vehicle]): Factory(Truck),
    (Mike, PetPicker, Type[Pet]): Factory(Dog),
}

world_two = Definitions(
    pet_defs,
    vehicle_defs,
    common_defs,
    world_two_defs,
)

logging.basicConfig(format='%(message)s', level=logging.INFO)

@inject(world_two)
def do(bob: Bob, mike: Mike):
    bob.deliver(100, 5, False)
    bob.deliver(50, 200, True)

    mike.deliver(20, 1000, True)

do()