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    "info": {
        "author": "Ilya Lebedev",
        "author_email": "lebedev.games.mail@gmail.com",
        "bugtrack_url": null,
        "classifiers": [
            "License :: OSI Approved :: MIT License",
            "Programming Language :: Python :: 3.6",
            "Programming Language :: Python :: 3.7",
            "Programming Language :: Python :: 3.8"
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        "description": "# Botox\n\nBotox Injector is a dependency injection implementation based on Python type annotations.\nIt helps deliver the configured functional objects, decreasing coupling between a class and its dependencies.\n\n### Delivery \n\nGlobal variables? Proxy objects? Application context?\n\nThere should be one (and preferably only one) obvious way to do it.\n\nBotox helps you isolate a class from the impact of different design changes and defects. \nMeaning that, instead of thinking about interdependence between application modules\nyou will now find yourself spending your time having to focus a class on the task it is designed for.\n\n### Configuration\n\nMonkey-patching? Decorators?\n\nExplicit is better than implicit.\n\nBotox allows a class the flexibility of being configurable.\nThe class rely on dependencies interface that he expect.\nExplicit configurations can be written separately for different\nsituations that require different implementations of dependencies.\n\n### Usage\n\nNow is better than never.\n\nBotox doesn\u2019t require any change in code behavior it can be applied to legacy code as a refactoring.\n\n## Installation\n\nInstall and update using [pip](https://pip.pypa.io/en/stable/quickstart/):\n\n```bash\npip install -U botox-di\n```\n\n## Examples\n\n### Class Injection\n\nCan be used to reduce boilerplate code in the application classes since \nall work to initialize or set up dependencies is handled separately.\n\n```python\nfrom botox import Injector\n\nclass PaymentService:\n    ...\n\nclass BillingService:\n    ...\n\nclass SalesService:\n\n    def __init__(self, payment_service: PaymentService, billing_service: BillingService):\n        self.payment_service = payment_service\n        self.billing_service = billing_service\n\ninjector = Injector()\ninjector.prepare(PaymentService)\ninjector.prepare(BillingService)\ninjector.prepare(SalesService)\n\nsales = injector.deliver(SalesService)\n\nassert isinstance(sales.payment_service, PaymentService)\nassert isinstance(sales.billing_service, BillingService)\n```\n\nThe result is class that is easier to unit test in \nisolation using stubs or mock objects that simulate other objects.\n\n```python\ninjector.prepare(PaymentService, PaymentServiceStub)\n```\n\n### Value Injection\n\nCan be used when exactly one object is needed to coordinate actions across the system.\n\n```python\nfrom botox import Injector\n\nclass AppSettings:\n    ...\n\nsettings = AppSettings()\n\ninjector = Injector()\ninjector.prepare(AppSettings, settings)\n\nassert injector.deliver(AppSettings) is settings\n```\n\n### Lambda Injection\n\nCan be used to wrap Proxy objects in legacy code as refactoring.\n\n```python\nfrom botox import Injector\nfrom flask import g\nfrom sqlalchemy.orm import Session\n\ninjector = Injector()\ninjector.prepare(Session, lambda: g.session)\n```\n\n### Function Injection\n\nCan be used to make factory functions with dependencies.\n\n```python\nfrom botox import Injector\n\ndef create_api_client(settings: Settings):\n    return ApiClient(settings.base_url, settings.key)\n\ninjector = Injector()\ninjector.prepare(Settings)\ninjector.prepare(ApiClient, create_api_client)\n```\n\n### Sequence Injection\n\nCan be used to provide collection of dependencies.\n\n```python\nclass PaymentService(metaclass=ABCMeta):\n    name: str\n\nclass GooglePayService(PaymentService):\n    ...\n\nclass ApplePayService(PaymentService):\n    ...\n\nclass FlowService:\n\n    def __init__(self, payment_services: List[PaymentService]):\n        self.payment_services = payment_services\n\n    def get_available_payment_methods(self):\n        for payment_service in self.payment_services:\n            yield payment_service.name\n\ninjector = Injector()\ninjector.prepare(GooglePayService)\ninjector.prepare(ApplePayService)\ninjector.prepare(List[PaymentService], [GooglePayService, ApplePayService])\ninjector.prepare(FlowService)\n\n```\n\n### Celery\n\nYou can define a different application base task class to deliver dependencies into a task call.\n\n```python\nfrom celery import Celery, Task\nfrom botox import Injector\n\nclass Calculator:\n    def add(self, x, y):\n        return x + y\n\nclass AppTask(Task):\n    def __call__(self, *args, **kwargs):\n        run = self.app.injector.inject(self.run, skip=len(args))\n        return run(*args, **kwargs)\n\napp = Celery('tasks', broker='pyamqp://guest@localhost//', task_cls=AppTask)\napp.injector = Injector()\napp.injector.prepare(Calculator)\n\n@app.task\ndef add(x, y, calculator: Calculator):\n    return calculator.add(x, y)\n```\n\n### aiohttp\n\nYou can use a middleware to deliver dependencies into a request handler. Asynchronous functions also supported.\n\n```python\nfrom aiohttp import web\nfrom botox import Injector\n\nclass HelloService:\n    def get_hello_message(self, name):\n        return f'Hello, {name}!'\n\nasync def handle(request, service: HelloService):\n    name = request.match_info.get('name', 'Anonymous')\n    text = service.get_hello_message(name)\n    return web.Response(text=text)\n\n@web.middleware\nasync def dependency_injection(request, handler):\n    handler = request.app.injector.inject(handler, skip=1)\n    return await handler(request)\n\napp = web.Application(middlewares=[dependency_injection])\napp.injector = Injector()\napp.injector.prepare(HelloService)\napp.add_routes([\n    web.get('/', handle),\n    web.get('/{name}', handle)\n])\n\nweb.run_app(app)\n```\n\n\n",
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