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.coveragerc Normal file
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[run]
include = dependency_injector/*
omit = tests/*

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version = 1
test_patterns = ["tests/**/test_*.py"]
exclude_patterns = ["docs/**"]
[[analyzers]]
name = "python"
enabled = true
[analyzers.meta]
runtime_version = "3.x.x"

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.github/FUNDING.yml vendored
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github: rmk135

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name: Publishing
on:
workflow_dispatch:
push:
tags:
- '*'
jobs:
tests:
name: Run tests
runs-on: ubuntu-24.04
steps:
- uses: actions/checkout@v3
- uses: actions/setup-python@v4
with:
python-version: 3.13
- run: pip install tox
- run: tox
env:
TOXENV: 3.13
linters:
name: Run linters
runs-on: ubuntu-24.04
strategy:
matrix:
toxenv: [flake8, pydocstyle, mypy, pylint]
steps:
- uses: actions/checkout@v3
- uses: actions/setup-python@v4
with:
python-version: 3.13
- run: pip install tox
- run: tox
env:
TOXENV: ${{ matrix.toxenv }}
build-sdist:
name: Build source tarball
needs: [tests, linters]
runs-on: ubuntu-24.04
steps:
- uses: actions/checkout@v3
- uses: actions/setup-python@v4
with:
python-version: 3.13
- run: |
python -m pip install --upgrade build
python -m build --sdist
- uses: actions/upload-artifact@v4
with:
name: cibw-sdist
path: ./dist/*
build-wheels:
name: Build wheels
needs: [tests, linters]
runs-on: ${{ matrix.os }}
strategy:
matrix:
os: [ubuntu-24.04, ubuntu-24.04-arm, windows-2019, macos-14]
env:
CIBW_SKIP: cp27-*
steps:
- uses: actions/checkout@v3
- name: Build wheels
uses: pypa/cibuildwheel@v2.20.0
- uses: actions/upload-artifact@v4
with:
name: cibw-wheels-x86-${{ matrix.os }}-${{ strategy.job-index }}
path: ./wheelhouse/*.whl
publish:
name: Publish on PyPI
needs: [build-sdist, build-wheels]
runs-on: ubuntu-24.04
steps:
- uses: actions/download-artifact@v4
with:
pattern: cibw-*
path: dist
merge-multiple: true
- uses: pypa/gh-action-pypi-publish@release/v1
with:
user: __token__
password: ${{ secrets.PYPI_API_TOKEN }}
# For publishing to Test PyPI, uncomment next two lines:
# password: ${{ secrets.TEST_PYPI_API_TOKEN }}
# repository_url: https://test.pypi.org/legacy/
publish-docs:
name: Publish docs
needs: [publish]
runs-on: ubuntu-24.04
steps:
- uses: actions/checkout@v3
- uses: actions/setup-python@v4
with:
python-version: 3.13
- run: pip install awscli
- run: pip install -r requirements-doc.txt
- run: pip install -e .
- run: (cd docs && make clean html)
- run: |
aws s3 sync docs/_build/html s3://python-dependency-injector-docs --delete
aws cloudfront create-invalidation --distribution-id ${{ secrets.AWS_CLOUDFRONT_DISTRIBUTION_ID }} --path "/*" > /dev/null
echo "Cache invalidation triggered"
env:
AWS_ACCESS_KEY_ID: ${{ secrets.AWS_ACCESS_KEY_ID }}
AWS_SECRET_ACCESS_KEY: ${{ secrets.AWS_SECRET_ACCESS_KEY }}
AWS_DEFAULT_REGION: ${{ secrets.AWS_DEFAULT_REGION }}

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name: Tests and linters
on: [push, pull_request, workflow_dispatch]
jobs:
tests-on-legacy-versions:
name: Run tests on legacy versions
runs-on: ubuntu-20.04
strategy:
matrix:
python-version: [3.7]
steps:
- uses: actions/checkout@v3
- uses: actions/setup-python@v4
with:
python-version: ${{ matrix.python-version }}
- run: pip install tox
- run: tox
env:
TOXENV: ${{ matrix.python-version }}
test-on-different-versions:
name: Run tests
runs-on: ubuntu-latest
strategy:
matrix:
python-version: [3.8, 3.9, "3.10", 3.11, 3.12, 3.13]
steps:
- uses: actions/checkout@v3
- uses: actions/setup-python@v4
with:
python-version: ${{ matrix.python-version }}
- run: pip install tox
- run: tox
env:
TOXENV: ${{ matrix.python-version }}
test-different-pydantic-versions:
name: Run tests with different pydantic versions
runs-on: ubuntu-latest
steps:
- uses: actions/checkout@v3
- uses: actions/setup-python@v4
with:
python-version: "3.12"
- run: pip install tox
- run: tox -e pydantic-v1,pydantic-v2
test-coverage:
name: Run tests with coverage
runs-on: ubuntu-latest
env:
DEPENDENCY_INJECTOR_DEBUG_MODE: 1
PIP_VERBOSE: 1
COVERALLS_REPO_TOKEN: ${{ secrets.COVERALLS_REPO_TOKEN }}
GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
steps:
- uses: actions/checkout@v3
- uses: actions/setup-python@v4
with:
python-version: 3.12
- run: pip install tox 'cython>=3,<4'
- run: tox -vv
env:
TOXENV: coveralls
linters:
name: Run linters
runs-on: ubuntu-latest
strategy:
matrix:
toxenv: [flake8, pydocstyle, mypy, pylint]
steps:
- uses: actions/checkout@v3
- uses: actions/setup-python@v4
with:
python-version: 3.13
- run: pip install tox
- run: tox
env:
TOXENV: ${{ matrix.toxenv }}

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.gitignore vendored
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__pycache__/
*.py[cod]
# C extensions
*.so
# Distribution / packaging
.Python
env/
@ -30,13 +33,12 @@ pip-log.txt
pip-delete-this-directory.txt
# Unit test / coverage reports
reports/
htmlcov/
.tox/
.coverage
.cache
nosetests.xml
coverage.xml
.hypothesis/
# Translations
*.mo
@ -55,21 +57,13 @@ target/
.idea/
# Virtualenv
venv*/
venv/
# SQLite
*.db
# JointJS Experiments
jointjs/
# Vim Rope
.ropeproject/
# Cython artifacts
src/**/*.c
src/**/*.h
src/**/*.so
src/**/*.html
# Workspace for samples
.workspace/
.vscode/

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.pylintrc Normal file
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[MASTER]
# Add <file or directory> to the black list. It should be a base name, not a
# path. You may set this option multiple times.
ignore=utils,test
[MESSAGES CONTROL]
# Disable the message(s) with the given id(s).
# disable-msg=
[SIMILARITIES]
# Minimum lines number of a similarity.
min-similarity-lines=5
[TYPECHECK]
ignore-mixin-members=yes
# ignored-classes=
zope=no
# generated-members=providedBy,implementedBy,rawDataReceived
[DESIGN]
# Maximum number of arguments for function / method
max-args=10
# Maximum number of locals for function / method body
max-locals=20
# Maximum number of return / yield for function / method body
max-returns=10
# Maximum number of branch for function / method body
max-branchs=10
# Maximum number of statements in function / method body
max-statements=60
# Maximum number of parents for a class (see R0901).
max-parents=10
# Maximum number of attributes for a class (see R0902).
max-attributes=30
# Minimum number of public methods for a class (see R0903).
min-public-methods=0
# Maximum number of public methods for a class (see R0904).
max-public-methods=30

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.travis.yml Normal file
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sudo: false
language: python
install: pip install tox
script: tox
python:
- 3.5
env:
- TOXENV=coveralls
- TOXENV=pylint
- TOXENV=flake8
- TOXENV=pydocstyle
- TOXENV=py26
- TOXENV=py27
- TOXENV=py33
- TOXENV=py34
- TOXENV=py35
- TOXENV=pypy
- TOXENV=pypy3

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Dependency Injector Contributors
================================
+ Roman Mogylatov (rmk135)
+ Konstantin vz'One Enchant (sirkonst)
+ Terrence Brannon (metaperl)
+ Stanislav Lobanov (asyncee)
+ James Lafa (jameslafa)
+ Vlad Ghita (vlad-ghita)
+ Jeroen Rietveld (jeroenrietveld)
+ Dmitry Kuzmin (xotonic)
+ supakeen (supakeen)
+ Bruno P. Kinoshita (kinow)
+ RobinsonMa (RobinsonMa)
+ Rüdiger Busche (JarnoRFB)
+ Dmitry Rassoshenko (rda-dev)
+ Fotis Koutoupas (kootoopas)
+ Shubhendra Singh Chauhan (withshubh)
+ sonthonaxrk (sonthonaxrk)
+ Ngo Thanh Loi (Leonn) (loingo95)
+ Thiago Hiromi (thiromi)
+ Felipe Rubio (krouw)
+ Anton Petrov (anton-petrov)
+ ZipFile (ZipFile)
+ Roman Mogilatov
+ Konstantin vz'One Enchant

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Copyright (c) 2024, Roman Mogylatov
Copyright (c) 2015, ETS Labs
All rights reserved.
Redistribution and use in source and binary forms, with or without

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recursive-include src/dependency_injector *.py* *.c
recursive-include tests *.py
include dependency_injector/*
include README.rst
include CONTRIBUTORS.rst
include LICENSE.rst
include requirements.txt
include setup.py
include tox.ini
include py.typed

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VERSION := $(shell python setup.py --version)
export COVERAGE_RCFILE := pyproject.toml
clean:
# Clean sources
find src -name '*.py[cod]' -delete
find src -name '__pycache__' -delete
find src -name '*.c' -delete
find src -name '*.h' -delete
find src -name '*.so' -delete
find src -name '*.html' -delete
# Clean tests
find tests -name '*.py[co]' -delete
find tests -name '__pycache__' -delete
# Clean examples
find examples -name '*.py[co]' -delete
find examples -name '__pycache__' -delete
build: clean
# Compile C extensions
python setup.py build_ext --inplace
# Move all Cython html reports
mkdir -p reports/cython/
find src -name '*.html' -exec mv {} reports/cython/ \;
docs-live:
sphinx-autobuild docs docs/_build/html
install: uninstall clean build
pip install -ve .
uninstall:
- pip uninstall -y -q dependency-injector 2> /dev/null
test:
# Unit tests with coverage report
coverage erase
coverage run -m pytest -c tests/.configs/pytest.ini
coverage report
coverage html
check:
flake8 src/dependency_injector/
flake8 examples/
pydocstyle src/dependency_injector/
pydocstyle examples/
mypy tests/typing
test-publish: build
# Create distributions
python -m build --sdist
# Upload distributions to PyPI
twine upload --repository testpypi dist/dependency-injector-$(VERSION)*
publish:
# Merge release to master branch
git checkout master
git merge --no-ff release/$(VERSION) -m "Merge branch 'release/$(VERSION)' into master"
git push origin master
# Create and upload tag
git tag -a $(VERSION) -m 'version $(VERSION)'
git push --tags

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.. figure:: https://raw.githubusercontent.com/wiki/ets-labs/python-dependency-injector/img/logo.svg
:target: https://github.com/ets-labs/python-dependency-injector
====================================================================
Dependency Injector - Dependency injection microframework for Python
====================================================================
|
*Dependency Injector* is a dependency injection microframework for Python.
It was designed to be unified, developer-friendly tool that helps to implement
dependency injection pattern in formal, pretty, Pythonic way.
.. image:: https://img.shields.io/pypi/v/dependency_injector.svg
:target: https://pypi.org/project/dependency-injector/
:alt: Latest Version
.. image:: https://img.shields.io/pypi/l/dependency_injector.svg
:target: https://pypi.org/project/dependency-injector/
:alt: License
*Dependency Injector* framework key features are:
.. image:: https://img.shields.io/pypi/pyversions/dependency_injector.svg
:target: https://pypi.org/project/dependency-injector/
:alt: Supported Python versions
.. image:: https://img.shields.io/pypi/implementation/dependency_injector.svg
:target: https://pypi.org/project/dependency-injector/
:alt: Supported Python implementations
+ Easy, smart, pythonic style.
+ Obvious, clear structure.
+ Extensibility and flexibility.
+ Memory efficiency.
+ Thread safety.
+ Documentation.
+ Semantic versioning.
.. image:: https://pepy.tech/badge/dependency-injector
:target: https://pepy.tech/project/dependency-injector
:alt: Downloads
Status
------
.. image:: https://pepy.tech/badge/dependency-injector/month
:target: https://pepy.tech/project/dependency-injector
:alt: Downloads
+---------------------------------------+----------------------------------------------------------------------------------------+
| *PyPi* | .. image:: https://img.shields.io/pypi/v/dependency_injector.svg |
| | :target: https://pypi.python.org/pypi/dependency_injector/ |
| | :alt: Latest Version |
| | .. image:: https://img.shields.io/pypi/l/dependency_injector.svg |
| | :target: https://pypi.python.org/pypi/dependency_injector/ |
| | :alt: License |
+---------------------------------------+----------------------------------------------------------------------------------------+
| *Python versions and implementations* | .. image:: https://img.shields.io/pypi/pyversions/dependency_injector.svg |
| | :target: https://pypi.python.org/pypi/dependency_injector/ |
| | :alt: Supported Python versions |
| | .. image:: https://img.shields.io/pypi/implementation/dependency_injector.svg |
| | :target: https://pypi.python.org/pypi/dependency_injector/ |
| | :alt: Supported Python implementations |
+---------------------------------------+----------------------------------------------------------------------------------------+
| *Builds and tests coverage* | .. image:: https://travis-ci.org/ets-labs/python-dependency-injector.svg?branch=master |
| | :target: https://travis-ci.org/ets-labs/python-dependency-injector |
| | :alt: Build Status |
| | .. image:: https://coveralls.io/repos/ets-labs/python-dependency-injector/badge.svg |
| | :target: https://coveralls.io/r/ets-labs/python-dependency-injector |
| | :alt: Coverage Status |
+---------------------------------------+----------------------------------------------------------------------------------------+
.. image:: https://pepy.tech/badge/dependency-injector/week
:target: https://pepy.tech/project/dependency-injector
:alt: Downloads
Dependency injection
--------------------
.. image:: https://img.shields.io/pypi/wheel/dependency-injector.svg
:target: https://pypi.org/project/dependency-injector/
:alt: Wheel
`Dependency injection`_ is a software design pattern that implements
`Inversion of control`_ for resolving dependencies. Formally, if object **A**
depends on object **B**, object **A** must not create or import object **B**
directly. Instead of this object **A** must provide a way for *injecting*
object **B**. The responsibilities of objects creation and dependencies
injection are delegated to external code - the *dependency injector*.
.. image:: https://img.shields.io/github/actions/workflow/status/ets-labs/python-dependency-injector/tests-and-linters.yml?branch=master
:target: https://github.com/ets-labs/python-dependency-injector/actions
:alt: Build Status
Popular terminology of dependency injection pattern:
.. image:: https://coveralls.io/repos/github/ets-labs/python-dependency-injector/badge.svg?branch=master
:target: https://coveralls.io/github/ets-labs/python-dependency-injector?branch=master
:alt: Coverage Status
+ Object **A**, that is dependant on object **B**, is often called -
the *client*.
+ Object **B**, that is a dependency, is often called - the *service*.
+ External code that is responsible for creation of objects and injection
of dependencies is often called - the *dependency injector*.
What is ``Dependency Injector``?
================================
There are several ways of how *service* can be injected into the *client*:
``Dependency Injector`` is a dependency injection framework for Python.
+ by passing it as ``__init__`` argument (constructor / initializer injection)
+ by setting it as attribute's value (attribute injection)
+ by passing it as method's argument (method injection)
It helps implement the dependency injection principle.
Dependency injection pattern has few strict rules that should be followed:
Key features of the ``Dependency Injector``:
+ The *client* delegates to the *dependency injector* the responsibility
of injecting its dependencies - the *service(s)*.
+ The *client* doesn't know how to create the *service*, it knows only
interface of the *service*. The *service* doesn't know that it is used by
the *client*.
+ The *dependency injector* knows how to create the *client* and
the *service*, it also knows that the *client* depends on the *service*,
and knows how to inject the *service* into the *client*.
+ The *client* and the *service* know nothing about the *dependency injector*.
- **Providers**. Provides ``Factory``, ``Singleton``, ``Callable``, ``Coroutine``, ``Object``,
``List``, ``Dict``, ``Configuration``, ``Resource``, ``Dependency``, and ``Selector`` providers
that help assemble your objects.
See `Providers <https://python-dependency-injector.ets-labs.org/providers/index.html>`_.
- **Overriding**. Can override any provider by another provider on the fly. This helps in testing
and configuring dev/stage environment to replace API clients with stubs etc. See
`Provider overriding <https://python-dependency-injector.ets-labs.org/providers/overriding.html>`_.
- **Configuration**. Reads configuration from ``yaml``, ``ini``, and ``json`` files, ``pydantic`` settings,
environment variables, and dictionaries.
See `Configuration provider <https://python-dependency-injector.ets-labs.org/providers/configuration.html>`_.
- **Resources**. Helps with initialization and configuring of logging, event loop, thread
or process pool, etc. Can be used for per-function execution scope in tandem with wiring.
See `Resource provider <https://python-dependency-injector.ets-labs.org/providers/resource.html>`_.
- **Containers**. Provides declarative and dynamic containers.
See `Containers <https://python-dependency-injector.ets-labs.org/containers/index.html>`_.
- **Wiring**. Injects dependencies into functions and methods. Helps integrate with
other frameworks: Django, Flask, Aiohttp, Sanic, FastAPI, etc.
See `Wiring <https://python-dependency-injector.ets-labs.org/wiring.html>`_.
- **Asynchronous**. Supports asynchronous injections.
See `Asynchronous injections <https://python-dependency-injector.ets-labs.org/providers/async.html>`_.
- **Typing**. Provides typing stubs, ``mypy``-friendly.
See `Typing and mypy <https://python-dependency-injector.ets-labs.org/providers/typing_mypy.html>`_.
- **Performance**. Fast. Written in ``Cython``.
- **Maturity**. Mature and production-ready. Well-tested, documented, and supported.
Dependency injection pattern provides next advantages:
+ Control on application structure.
+ Decreased coupling between application components.
+ Increased code reusability.
+ Increased testability.
+ Increased maintainability.
+ Reconfiguration of system without rebuilding.
Example of dependency injection
-------------------------------
Brief example below demonstrates usage of *Dependency Injector* for creating
several IoC containers for some example application:
.. code-block:: python
from dependency_injector import containers, providers
from dependency_injector.wiring import Provide, inject
"""Example of dependency injection in Python."""
import logging
import sqlite3
import boto.s3.connection
import example.main
import example.services
import dependency_injector.containers as containers
import dependency_injector.providers as providers
class Container(containers.DeclarativeContainer):
class Platform(containers.DeclarativeContainer):
"""IoC container of platform service providers."""
config = providers.Configuration()
logger = providers.Singleton(logging.Logger, name='example')
api_client = providers.Singleton(
ApiClient,
api_key=config.api_key,
timeout=config.timeout,
)
database = providers.Singleton(sqlite3.connect, ':memory:')
service = providers.Factory(
Service,
api_client=api_client,
)
s3 = providers.Singleton(boto.s3.connection.S3Connection,
aws_access_key_id='KEY',
aws_secret_access_key='SECRET')
@inject
def main(service: Service = Provide[Container.service]) -> None:
...
class Services(containers.DeclarativeContainer):
"""IoC container of business service providers."""
users = providers.Factory(example.services.Users,
logger=Platform.logger,
db=Platform.database)
auth = providers.Factory(example.services.Auth,
logger=Platform.logger,
db=Platform.database,
token_ttl=3600)
photos = providers.Factory(example.services.Photos,
logger=Platform.logger,
db=Platform.database,
s3=Platform.s3)
if __name__ == "__main__":
container = Container()
container.config.api_key.from_env("API_KEY", required=True)
container.config.timeout.from_env("TIMEOUT", as_=int, default=5)
container.wire(modules=[__name__])
class Application(containers.DeclarativeContainer):
"""IoC container of application component providers."""
main() # <-- dependency is injected automatically
main = providers.Callable(example.main.main,
users_service=Services.users,
auth_service=Services.auth,
photos_service=Services.photos)
with container.api_client.override(mock.Mock()):
main() # <-- overridden dependency is injected automatically
Next example demonstrates run of example application defined above:
When you call the ``main()`` function the ``Service`` dependency is assembled and injected automatically.
.. code-block:: python
When you do testing, you call the ``container.api_client.override()`` method to replace the real API
client with a mock. When you call ``main()``, the mock is injected.
"""Run example application."""
You can override any provider with another provider.
import sys
import logging
It also helps you in a re-configuring project for different environments: replace an API client
with a stub on the dev or stage.
from containers import Platform, Application
With the ``Dependency Injector``, object assembling is consolidated in a container. Dependency injections are defined explicitly.
This makes it easier to understand and change how an application works.
.. figure:: https://raw.githubusercontent.com/wiki/ets-labs/python-dependency-injector/img/di-readme.svg
:target: https://github.com/ets-labs/python-dependency-injector
if __name__ == '__main__':
# Configure platform logger:
Platform.logger().addHandler(logging.StreamHandler(sys.stdout))
Visit the docs to know more about the
`Dependency injection and inversion of control in Python <https://python-dependency-injector.ets-labs.org/introduction/di_in_python.html>`_.
# Run application:
Application.main(uid=sys.argv[1],
password=sys.argv[2],
photo=sys.argv[3])
# Previous call is an equivalent of next operations:
#
# logger = logging.Logger(name='example')
# database = sqlite3.connect(':memory:')
# s3 = boto.s3.connection.S3Connection(aws_access_key_id='KEY',
# aws_secret_access_key='SECRET')
#
# example.main.main(uid=sys.argv[1],
# password=sys.argv[2],
# photo=sys.argv[3],
# users_service=example.services.Users(logger=logger,
# db=database),
# auth_service=example.services.Auth(logger=logger,
# db=database,
# token_ttl=3600),
# photos_service=example.services.Photos(logger=logger,
# db=database,
# s3=s3))
Alternative definition styles of providers
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
*Dependecy Injector* supports few other styles of dependency injections
definition.
IoC containers from previous example could look like these:
.. code-block:: python
class Platform(containers.DeclarativeContainer):
"""IoC container of platform service providers."""
logger = providers.Singleton(logging.Logger) \
.add_kwargs(name='example')
database = providers.Singleton(sqlite3.connect) \
.add_args(':memory:')
s3 = providers.Singleton(boto.s3.connection.S3Connection) \
.add_kwargs(aws_access_key_id='KEY',
aws_secret_access_key='SECRET')
or like this these:
.. code-block:: python
class Platform(containers.DeclarativeContainer):
"""IoC container of platform service providers."""
logger = providers.Singleton(logging.Logger)
logger.add_kwargs(name='example')
database = providers.Singleton(sqlite3.connect)
database.add_args(':memory:')
s3 = providers.Singleton(boto.s3.connection.S3Connection)
s3.add_kwargs(aws_access_key_id='KEY',
aws_secret_access_key='SECRET')
You can get more *Dependency Injector* examples in ``/examples`` directory on
GitHub:
https://github.com/ets-labs/python-dependency-injector
Installation
------------
The package is available on the `PyPi`_::
*Dependency Injector* library is available on `PyPi`_::
pip install dependency-injector
pip install dependency_injector
Documentation
-------------
The documentation is available `here <https://python-dependency-injector.ets-labs.org/>`_.
*Dependency Injector* documentation is hosted on ReadTheDocs:
Examples
--------
- `User's guide`_
- `API docs`_
Choose one of the following:
Feedback & Support
------------------
- `Application example (single container) <https://python-dependency-injector.ets-labs.org/examples/application-single-container.html>`_
- `Application example (multiple containers) <https://python-dependency-injector.ets-labs.org/examples/application-multiple-containers.html>`_
- `Decoupled packages example (multiple containers) <https://python-dependency-injector.ets-labs.org/examples/decoupled-packages.html>`_
- `Boto3 example <https://python-dependency-injector.ets-labs.org/examples/boto3.html>`_
- `Django example <https://python-dependency-injector.ets-labs.org/examples/django.html>`_
- `Flask example <https://python-dependency-injector.ets-labs.org/examples/flask.html>`_
- `Aiohttp example <https://python-dependency-injector.ets-labs.org/examples/aiohttp.html>`_
- `Sanic example <https://python-dependency-injector.ets-labs.org/examples/sanic.html>`_
- `FastAPI example <https://python-dependency-injector.ets-labs.org/examples/fastapi.html>`_
- `FastAPI + Redis example <https://python-dependency-injector.ets-labs.org/examples/fastapi-redis.html>`_
- `FastAPI + SQLAlchemy example <https://python-dependency-injector.ets-labs.org/examples/fastapi-sqlalchemy.html>`_
Feel free to post questions, bugs, feature requests, proposals etc. on
*Dependency Injector* GitHub Issues:
Tutorials
---------
https://github.com/ets-labs/python-dependency-injector/issues
Choose one of the following:
Your feedback is quite important!
- `Flask web application tutorial <https://python-dependency-injector.ets-labs.org/tutorials/flask.html>`_
- `Aiohttp REST API tutorial <https://python-dependency-injector.ets-labs.org/tutorials/aiohttp.html>`_
- `Asyncio monitoring daemon tutorial <https://python-dependency-injector.ets-labs.org/tutorials/asyncio-daemon.html>`_
- `CLI application tutorial <https://python-dependency-injector.ets-labs.org/tutorials/cli.html>`_
Concept
-------
The framework stands on the `PEP20 (The Zen of Python) <https://www.python.org/dev/peps/pep-0020/>`_ principle:
.. code-block:: bash
Explicit is better than implicit
You need to specify how to assemble and where to inject the dependencies explicitly.
The power of the framework is in its simplicity.
``Dependency Injector`` is a simple tool for the powerful concept.
Frequently asked questions
--------------------------
What is dependency injection?
- dependency injection is a principle that decreases coupling and increases cohesion
Why should I do the dependency injection?
- your code becomes more flexible, testable, and clear 😎
How do I start applying the dependency injection?
- you start writing the code following the dependency injection principle
- you register all of your application components and their dependencies in the container
- when you need a component, you specify where to inject it or get it from the container
What price do I pay and what do I get?
- you need to explicitly specify the dependencies
- it will be extra work in the beginning
- it will payoff as project grows
Have a question?
- Open a `Github Issue <https://github.com/ets-labs/python-dependency-injector/issues>`_
Found a bug?
- Open a `Github Issue <https://github.com/ets-labs/python-dependency-injector/issues>`_
Want to help?
- |star| Star the ``Dependency Injector`` on the `Github <https://github.com/ets-labs/python-dependency-injector/>`_
- |new| Start a new project with the ``Dependency Injector``
- |tell| Tell your friend about the ``Dependency Injector``
Want to contribute?
- |fork| Fork the project
- |pull| Open a pull request to the ``develop`` branch
.. _PyPi: https://pypi.org/project/dependency-injector/
.. |star| unicode:: U+2B50 U+FE0F .. star sign1
.. |new| unicode:: U+1F195 .. new sign
.. |tell| unicode:: U+1F4AC .. tell sign
.. |fork| unicode:: U+1F500 .. fork sign
.. |pull| unicode:: U+2B05 U+FE0F .. pull sign
.. _Dependency injection: http://en.wikipedia.org/wiki/Dependency_injection
.. _Inversion of control: https://en.wikipedia.org/wiki/Inversion_of_control
.. _PyPi: https://pypi.python.org/pypi/dependency_injector
.. _User's guide: http://python-dependency-injector.ets-labs.org/en/stable/
.. _API docs: http://python-dependency-injector.ets-labs.org/en/stable/api/

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@ -0,0 +1,7 @@
"""Dependency injector top-level package."""
VERSION = '2.2.8'
"""Version number that follows semantic versioning.
:type: str
"""

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@ -0,0 +1,363 @@
"""Dependency injector IoC containers module."""
import six
from dependency_injector import (
providers,
utils,
errors,
)
class DynamicContainer(object):
"""Dynamic inversion of control container.
.. code-block:: python
services = DynamicContainer()
services.auth = providers.Factory(AuthService)
services.users = providers.Factory(UsersService,
auth_service=services.auth)
.. py:attribute:: providers
Read-only dictionary of all providers.
:type: dict[str, :py:class:`dependency_injector.providers.Provider`]
.. py:attribute:: overridden
Tuple of overriding containers.
:type: tuple[:py:class:`DynamicContainer`]
.. py:attribute:: provider_type
Type of providers that could be placed in container.
:type: type
"""
__IS_CONTAINER__ = True
def __init__(self):
"""Initializer.
:rtype: None
"""
self.provider_type = providers.Provider
self.providers = dict()
self.overridden = tuple()
super(DynamicContainer, self).__init__()
def __setattr__(self, name, value):
"""Set instance attribute.
If value of attribute is provider, it will be added into providers
dictionary.
:param name: Attribute's name
:type name: str
:param value: Attribute's value
:type value: object
:rtype: None
"""
if utils.is_provider(value):
_check_provider_type(self, value)
self.providers[name] = value
super(DynamicContainer, self).__setattr__(name, value)
def __delattr__(self, name):
"""Delete instance attribute.
If value of attribute is provider, it will be deleted from providers
dictionary.
:param name: Attribute's name
:type name: str
:rtype: None
"""
if name in self.providers:
del self.providers[name]
super(DynamicContainer, self).__delattr__(name)
def override(self, overriding):
"""Override current container by overriding container.
:param overriding: Overriding container.
:type overriding: :py:class:`DynamicContainer`
:raise: :py:exc:`dependency_injector.errors.Error` if trying to
override container by itself
:rtype: None
"""
if overriding is self:
raise errors.Error('Container {0} could not be overridden '
'with itself'.format(self))
self.overridden += (overriding,)
for name, provider in six.iteritems(overriding.providers):
try:
getattr(self, name).override(provider)
except AttributeError:
pass
def reset_last_overriding(self):
"""Reset last overriding provider for each container providers.
:rtype: None
"""
if not self.overridden:
raise errors.Error('Container {0} is not overridden'.format(self))
self.overridden = self.overridden[:-1]
for provider in six.itervalues(self.providers):
provider.reset_last_overriding()
def reset_override(self):
"""Reset all overridings for each container providers.
:rtype: None
"""
self.overridden = tuple()
for provider in six.itervalues(self.providers):
provider.reset_override()
class DeclarativeContainerMetaClass(type):
"""Declarative inversion of control container meta class."""
def __new__(mcs, class_name, bases, attributes):
"""Declarative container class factory."""
cls_providers = tuple((name, provider)
for name, provider in six.iteritems(attributes)
if utils.is_provider(provider))
inherited_providers = tuple((name, provider)
for base in bases if utils.is_container(
base) and base is not DynamicContainer
for name, provider in six.iteritems(
base.cls_providers))
attributes['cls_providers'] = dict(cls_providers)
attributes['inherited_providers'] = dict(inherited_providers)
attributes['providers'] = dict(cls_providers + inherited_providers)
cls = type.__new__(mcs, class_name, bases, attributes)
for provider in six.itervalues(cls.providers):
_check_provider_type(cls, provider)
return cls
def __setattr__(cls, name, value):
"""Set class attribute.
If value of attribute is provider, it will be added into providers
dictionary.
:param name: Attribute's name
:type name: str
:param value: Attribute's value
:type value: object
:rtype: None
"""
if utils.is_provider(value):
_check_provider_type(cls, value)
cls.providers[name] = value
cls.cls_providers[name] = value
super(DeclarativeContainerMetaClass, cls).__setattr__(name, value)
def __delattr__(cls, name):
"""Delete class attribute.
If value of attribute is provider, it will be deleted from providers
dictionary.
:param name: Attribute's name
:type name: str
:rtype: None
"""
if name in cls.providers and name in cls.cls_providers:
del cls.providers[name]
del cls.cls_providers[name]
super(DeclarativeContainerMetaClass, cls).__delattr__(name)
@six.add_metaclass(DeclarativeContainerMetaClass)
class DeclarativeContainer(object):
"""Declarative inversion of control container.
.. code-block:: python
class Services(DeclarativeContainer):
auth = providers.Factory(AuthService)
users = providers.Factory(UsersService,
auth_service=auth)
"""
__IS_CONTAINER__ = True
provider_type = providers.Provider
"""Type of providers that could be placed in container.
:type: type
"""
instance_type = DynamicContainer
"""Type of container that is returned on instantiating declarative
container.
:type: type
"""
providers = dict()
"""Read-only dictionary of all providers.
:type: dict[str, :py:class:`dependency_injector.providers.Provider`]
"""
cls_providers = dict()
"""Read-only dictionary of current container providers.
:type: dict[str, :py:class:`dependency_injector.providers.Provider`]
"""
inherited_providers = dict()
"""Read-only dictionary of inherited providers.
:type: dict[str, :py:class:`dependency_injector.providers.Provider`]
"""
overridden = tuple()
"""Tuple of overriding containers.
:type: tuple[:py:class:`DeclarativeContainer`]
"""
def __new__(cls, *args, **kwargs):
"""Constructor.
:return: Dynamic container with copy of all providers.
:rtype: :py:class:`DynamicContainer`
"""
container = cls.instance_type(*args, **kwargs)
container.provider_type = cls.provider_type
for name, provider in six.iteritems(utils.deepcopy(cls.providers)):
setattr(container, name, provider)
return container
@classmethod
def override(cls, overriding):
"""Override current container by overriding container.
:param overriding: Overriding container.
:type overriding: :py:class:`DeclarativeContainer`
:raise: :py:exc:`dependency_injector.errors.Error` if trying to
override container by itself or its subclasses
:rtype: None
"""
if issubclass(cls, overriding):
raise errors.Error('Container {0} could not be overridden '
'with itself or its subclasses'.format(cls))
cls.overridden += (overriding,)
for name, provider in six.iteritems(overriding.cls_providers):
try:
getattr(cls, name).override(provider)
except AttributeError:
pass
@classmethod
def reset_last_overriding(cls):
"""Reset last overriding provider for each container providers.
:rtype: None
"""
if not cls.overridden:
raise errors.Error('Container {0} is not overridden'.format(cls))
cls.overridden = cls.overridden[:-1]
for provider in six.itervalues(cls.providers):
provider.reset_last_overriding()
@classmethod
def reset_override(cls):
"""Reset all overridings for each container providers.
:rtype: None
"""
cls.overridden = tuple()
for provider in six.itervalues(cls.providers):
provider.reset_override()
def override(container):
""":py:class:`DeclarativeContainer` overriding decorator.
:param container: Container that should be overridden by decorated
container.
:type container: :py:class:`DeclarativeContainer`
:return: Declarative container's overriding decorator.
:rtype: callable(:py:class:`DeclarativeContainer`)
"""
def _decorator(overriding_container):
"""Overriding decorator."""
container.override(overriding_container)
return overriding_container
return _decorator
def copy(container):
""":py:class:`DeclarativeContainer` copying decorator.
This decorator copy all providers from provided container to decorated one.
If one of the decorated container providers matches to source container
providers by name, it would be replaced by reference.
:param container: Container that should be copied by decorated container.
:type container: :py:class:`DeclarativeContainer`
:return: Declarative container's copying decorator.
:rtype: callable(:py:class:`DeclarativeContainer`)
"""
def _decorator(copied_container):
memo = dict()
for name, provider in six.iteritems(copied_container.cls_providers):
try:
source_provider = getattr(container, name)
except AttributeError:
pass
else:
memo[id(source_provider)] = provider
providers_copy = utils.deepcopy(container.providers, memo)
for name, provider in six.iteritems(providers_copy):
setattr(copied_container, name, provider)
return copied_container
return _decorator
def _check_provider_type(cls, provider):
if not isinstance(provider, cls.provider_type):
raise errors.Error('{0} can contain only {1} '
'instances'.format(cls, cls.provider_type))

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"""Dependency injector errors module."""
class Error(Exception):
"""Base error.
All dependency injector errors extend this error class.
"""

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"""Dependency injector injections module."""
import warnings
import six
from dependency_injector.providers.base import (
_parse_positional_injections,
_parse_keyword_injections,
)
from dependency_injector import utils
from dependency_injector import errors
def inject(*args, **kwargs):
"""Dependency injection decorator.
.. warning::
:py:func:`inject` decorator has been deprecated since version 2.2.0.
Usage of :py:func:`inject` decorator can lead to bad design and could
be considered as anti-pattern.
:py:func:`inject` decorator can be used for making inline dependency
injections. It patches decorated callable in such way that dependency
injection will be done during every call of decorated callable.
:py:func:`inject` decorator supports different syntaxes of passing
injections:
.. code-block:: python
# Positional arguments injections:
@inject(1, 2)
def some_function(arg1, arg2):
pass
# Keyword arguments injections:
@inject(arg1=1)
@inject(arg2=2)
def some_function(arg1, arg2):
pass
# Keyword arguments injections into class init:
@inject(arg1=1)
@inject(arg2=2)
class SomeClass(object):
def __init__(self, arg1, arg2):
pass
.. deprecated:: 2.2.0
Usage of :py:func:`inject` decorator can lead to bad design and could
be considered as anti-pattern.
:param args: Tuple of context positional arguments.
:type args: tuple[object]
:param kwargs: Dictionary of context keyword arguments.
:type kwargs: dict[str, object]
:return: Class / callable decorator
:rtype: (callable) -> (type | callable)
"""
warnings.warn(message='Call to a deprecated decorator - @{0}.{1}'
.format(inject.__module__, inject.__name__),
category=DeprecationWarning,
stacklevel=2)
arg_injections = _parse_positional_injections(args)
kwarg_injections = _parse_keyword_injections(kwargs)
def decorator(callback_or_cls):
"""Dependency injection decorator."""
if isinstance(callback_or_cls, six.class_types):
cls = callback_or_cls
cls_init = utils.fetch_cls_init(cls)
if not cls_init:
raise errors.Error(
'Class {0}.{1} has no __init__() '.format(cls.__module__,
cls.__name__) +
'method and could not be decorated with @inject decorator')
cls.__init__ = decorator(cls_init)
return cls
callback = callback_or_cls
if hasattr(callback, '__INJECT_DECORATED__'):
callback.args += arg_injections
callback.kwargs.update(kwarg_injections)
return callback
@six.wraps(callback)
def decorated(*args, **kwargs):
"""Decorated with dependency injection callback."""
if decorated.args:
args = tuple(arg.provide_injection()
for arg in decorated.args) + args
for name, arg in six.iteritems(decorated.kwargs):
if name not in kwargs:
kwargs[name] = arg.provide_injection()
return callback(*args, **kwargs)
decorated.__INJECT_DECORATED__ = True
decorated.origin = callback
decorated.args = arg_injections
decorated.kwargs = kwarg_injections
return decorated
return decorator

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"""Dependency injector providers package."""
from dependency_injector.providers.base import (
Provider,
Delegate,
Object,
ExternalDependency,
OverridingContext,
override,
)
from dependency_injector.providers.callable import (
Callable,
DelegatedCallable,
)
from dependency_injector.providers.creational import (
Factory,
DelegatedFactory,
Singleton,
DelegatedSingleton,
ThreadLocalSingleton,
DelegatedThreadLocalSingleton
)
__all__ = (
'Provider',
'Delegate',
'Object',
'ExternalDependency',
'OverridingContext',
'override',
'Callable',
'DelegatedCallable',
'Factory',
'DelegatedFactory',
'Singleton',
'DelegatedSingleton',
'ThreadLocalSingleton',
'DelegatedThreadLocalSingleton',
)

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@ -0,0 +1,416 @@
"""Dependency injector base providers."""
import six
from dependency_injector.errors import Error
from dependency_injector.utils import (
is_provider,
ensure_is_provider,
represent_provider,
)
@six.python_2_unicode_compatible
class Provider(object):
"""Base provider class.
:py:class:`Provider` is callable (implements ``__call__`` method). Every
call to provider object returns provided result, according to the providing
strategy of particular provider. This ``callable`` functionality is a
regular part of providers API and it should be the same for all provider's
subclasses.
Implementation of particular providing strategy should be done in
:py:meth:`Provider._provide` of :py:class:`Provider` subclass. Current
method is called every time when not overridden provider is called.
:py:class:`Provider` implements provider overriding logic that should be
also common for all providers:
.. code-block:: python
provider1 = Factory(SomeClass)
provider2 = Factory(ChildSomeClass)
provider1.override(provider2)
some_instance = provider1()
assert isinstance(some_instance, ChildSomeClass)
Also :py:class:`Provider` implements helper function for creating its
delegates:
.. code-block:: python
provider = Factory(object)
delegate = provider.delegate()
delegated = delegate()
assert provider is delegated
All providers should extend this class.
.. py:attribute:: overridden
Tuple of overriding providers, if any.
:type: tuple[:py:class:`Provider`] | None
"""
__IS_PROVIDER__ = True
__OPTIMIZED_CALLS__ = True
__slots__ = ('overridden', 'provide', '__call__')
def __init__(self):
"""Initializer."""
self.overridden = tuple()
super(Provider, self).__init__()
# Enable __call__() / _provide() optimization
if self.__class__.__OPTIMIZED_CALLS__:
self.__call__ = self.provide = self._provide
def _provide(self, *args, **kwargs):
"""Providing strategy implementation.
Abstract protected method that implements providing strategy of
particular provider. Current method is called every time when not
overridden provider is called. Need to be overridden in subclasses.
"""
raise NotImplementedError()
def _call_last_overriding(self, *args, **kwargs):
"""Call last overriding provider and return result."""
return (self.overridden[-1](*args, **kwargs)
if self.overridden
else None)
def provide_injection(self):
"""Injection strategy implementation.
:rtype: object
"""
return self.provide()
def override(self, provider):
"""Override provider with another provider.
:param provider: Overriding provider.
:type provider: :py:class:`Provider`
:raise: :py:exc:`dependency_injector.errors.Error`
:return: Overriding provider.
:rtype: :py:class:`Provider`
"""
if provider is self:
raise Error('Provider {0} could not be overridden '
'with itself'.format(self))
if not is_provider(provider):
provider = Object(provider)
self.overridden += (ensure_is_provider(provider),)
# Disable __call__() / _provide() optimization
if self.__class__.__OPTIMIZED_CALLS__:
self.__call__ = self.provide = self._call_last_overriding
return OverridingContext(self, provider)
def reset_last_overriding(self):
"""Reset last overriding provider.
:raise: :py:exc:`dependency_injector.errors.Error` if provider is not
overridden.
:rtype: None
"""
if not self.overridden:
raise Error('Provider {0} is not overridden'.format(str(self)))
self.overridden = self.overridden[:-1]
if not self.overridden:
# Enable __call__() / _provide() optimization
if self.__class__.__OPTIMIZED_CALLS__:
self.__call__ = self.provide = self._provide
def reset_override(self):
"""Reset all overriding providers.
:rtype: None
"""
self.overridden = tuple()
# Enable __call__() / _provide() optimization
if self.__class__.__OPTIMIZED_CALLS__:
self.__call__ = self.provide = self._provide
def delegate(self):
"""Return provider's delegate.
:rtype: :py:class:`Delegate`
"""
return Delegate(self)
def __str__(self):
"""Return string representation of provider.
:rtype: str
"""
return represent_provider(provider=self, provides=None)
__repr__ = __str__
@six.python_2_unicode_compatible
class Delegate(Provider):
""":py:class:`Delegate` provider delegates another provider.
.. code-block:: python
provider = Factory(object)
delegate = Delegate(provider)
delegated = delegate()
assert provider is delegated
.. py:attribute:: delegated
Delegated provider.
:type: :py:class:`Provider`
"""
__slots__ = ('delegated',)
def __init__(self, delegated):
"""Initializer.
:provider delegated: Delegated provider.
:type delegated: :py:class:`Provider`
"""
self.delegated = ensure_is_provider(delegated)
super(Delegate, self).__init__()
def _provide(self, *args, **kwargs):
"""Return provided instance.
:param args: Tuple of context positional arguments.
:type args: tuple[object]
:param kwargs: Dictionary of context keyword arguments.
:type kwargs: dict[str, object]
:rtype: object
"""
return self.delegated
def __str__(self):
"""Return string representation of provider.
:rtype: str
"""
return represent_provider(provider=self, provides=self.delegated)
__repr__ = __str__
@six.python_2_unicode_compatible
class Object(Provider):
""":py:class:`Object` provider returns provided instance "as is".
.. py:attribute:: provides
Value that have to be provided.
:type: object
"""
__slots__ = ('provides',)
def __init__(self, provides):
"""Initializer.
:param provides: Value that have to be provided.
:type provides: object
"""
self.provides = provides
super(Object, self).__init__()
def _provide(self, *args, **kwargs):
"""Return provided instance.
:param args: Tuple of context positional arguments.
:type args: tuple[object]
:param kwargs: Dictionary of context keyword arguments.
:type kwargs: dict[str, object]
:rtype: object
"""
return self.provides
def __str__(self):
"""Return string representation of provider.
:rtype: str
"""
return represent_provider(provider=self, provides=self.provides)
__repr__ = __str__
@six.python_2_unicode_compatible
class ExternalDependency(Provider):
""":py:class:`ExternalDependency` provider describes dependency interface.
This provider is used for description of dependency interface. That might
be useful when dependency could be provided in the client's code only,
but it's interface is known. Such situations could happen when required
dependency has non-determenistic list of dependencies itself.
.. code-block:: python
database_provider = ExternalDependency(sqlite3.dbapi2.Connection)
database_provider.override(Factory(sqlite3.connect, ':memory:'))
database = database_provider()
.. py:attribute:: instance_of
Class of required dependency.
:type: type
"""
__OPTIMIZED_CALLS__ = False
__slots__ = ('instance_of',)
def __init__(self, instance_of):
"""Initializer."""
if not isinstance(instance_of, six.class_types):
raise Error('ExternalDependency provider expects to get class, ' +
'got {0} instead'.format(str(instance_of)))
self.instance_of = instance_of
self.provide = self.__call__
super(ExternalDependency, self).__init__()
def __call__(self, *args, **kwargs):
"""Return provided instance.
:param args: Tuple of context positional arguments.
:type args: tuple[object]
:param kwargs: Dictionary of context keyword arguments.
:type kwargs: dict[str, object]
:raise: :py:exc:`dependency_injector.errors.Error`
:rtype: object
"""
if not self.overridden:
raise Error('Dependency is not defined')
instance = self._call_last_overriding(*args, **kwargs)
if not isinstance(instance, self.instance_of):
raise Error('{0} is not an '.format(instance) +
'instance of {0}'.format(self.instance_of))
return instance
def provided_by(self, provider):
"""Set external dependency provider.
:param provider: Provider that provides required dependency.
:type provider: :py:class:`Provider`
:rtype: None
"""
return self.override(provider)
def __str__(self):
"""Return string representation of provider.
:rtype: str
"""
return represent_provider(provider=self, provides=self.instance_of)
__repr__ = __str__
class OverridingContext(object):
"""Provider overriding context.
:py:class:`OverridingContext` is used by :py:meth:`Provider.override` for
implemeting ``with`` contexts. When :py:class:`OverridingContext` is
closed, overriding that was created in this context is dropped also.
.. code-block:: python
with provider.override(another_provider):
assert provider.overridden
assert not provider.overridden
"""
def __init__(self, overridden, overriding):
"""Initializer.
:param overridden: Overridden provider.
:type overridden: :py:class:`Provider`
:param overriding: Overriding provider.
:type overriding: :py:class:`Provider`
"""
self.overridden = overridden
self.overriding = overriding
def __enter__(self):
"""Do nothing."""
return self.overriding
def __exit__(self, *_):
"""Exit overriding context."""
self.overridden.reset_last_overriding()
def override(overridden):
"""Decorator for overriding providers.
This decorator overrides ``overridden`` provider by decorated one.
.. code-block:: python
@Factory
class SomeClass(object):
pass
@override(SomeClass)
@Factory
class ExtendedSomeClass(SomeClass.cls):
pass
:param overridden: Provider that should be overridden.
:type overridden: :py:class:`Provider`
:return: Overriding provider.
:rtype: :py:class:`Provider`
"""
def decorator(overriding):
overridden.override(overriding)
return overriding
return decorator
def _parse_positional_injections(args):
return tuple(arg if is_provider(arg) else Object(arg)
for arg in args)
def _parse_keyword_injections(kwargs):
return dict((name, arg if is_provider(arg) else Object(arg))
for name, arg in six.iteritems(kwargs))

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@ -0,0 +1,127 @@
"""Dependency injector callable providers."""
import six
from dependency_injector.providers.base import (
Provider,
_parse_positional_injections,
_parse_keyword_injections,
)
from dependency_injector.utils import represent_provider
from dependency_injector.errors import Error
@six.python_2_unicode_compatible
class Callable(Provider):
r""":py:class:`Callable` provider calls wrapped callable on every call.
:py:class:`Callable` supports positional and keyword argument injections:
.. code-block:: python
some_function = Callable(some_function,
'positional_arg1', 'positional_arg2',
keyword_argument1=3, keyword_argument=4)
# or
some_function = Callable(some_function) \
.add_args('positional_arg1', 'positional_arg2') \
.add_kwargs(keyword_argument1=3, keyword_argument=4)
# or
some_function = Callable(some_function)
some_function.add_args('positional_arg1', 'positional_arg2')
some_function.add_kwargs(keyword_argument1=3, keyword_argument=4)
"""
__slots__ = ('provides', 'args', 'kwargs')
def __init__(self, provides, *args, **kwargs):
"""Initializer.
:param provides: Wrapped callable.
:type provides: callable
"""
if not callable(provides):
raise Error('Provider {0} expected to get callable, '
'got {0}'.format('.'.join((self.__class__.__module__,
self.__class__.__name__)),
provides))
self.provides = provides
self.args = tuple()
self.kwargs = dict()
self.add_args(*args)
self.add_kwargs(**kwargs)
super(Callable, self).__init__()
def add_args(self, *args):
"""Add postional argument injections.
:param args: Tuple of injections.
:type args: tuple
:return: Reference ``self``
"""
self.args += _parse_positional_injections(args)
return self
def add_kwargs(self, **kwargs):
"""Add keyword argument injections.
:param kwargs: Dictionary of injections.
:type kwargs: dict
:return: Reference ``self``
"""
self.kwargs.update(_parse_keyword_injections(kwargs))
return self
def _provide(self, *args, **kwargs):
"""Return provided instance.
:param args: Tuple of context positional arguments.
:type args: tuple[object]
:param kwargs: Dictionary of context keyword arguments.
:type kwargs: dict[str, object]
:rtype: object
"""
if self.args:
args = tuple(arg.provide_injection() for arg in self.args) + args
for name, arg in six.iteritems(self.kwargs):
if name not in kwargs:
kwargs[name] = arg.provide_injection()
return self.provides(*args, **kwargs)
def __str__(self):
"""Return string representation of provider.
:rtype: str
"""
return represent_provider(provider=self, provides=self.provides)
__repr__ = __str__
class DelegatedCallable(Callable):
""":py:class:`DelegatedCallable` is a delegated :py:class:`Callable`.
:py:class:`DelegatedCallable` is a :py:class:`Callable`, that is injected
"as is".
"""
def provide_injection(self):
"""Injection strategy implementation.
:rtype: object
"""
return self

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@ -0,0 +1,342 @@
"""Dependency injector creational providers."""
import threading
import six
from dependency_injector.providers.callable import Callable
from dependency_injector.providers.base import _parse_keyword_injections
from dependency_injector.utils import GLOBAL_LOCK
from dependency_injector.errors import Error
class Factory(Callable):
r""":py:class:`Factory` provider creates new instance on every call.
:py:class:`Factory` supports positional & keyword argument injections,
as well as attribute injections.
Positional and keyword argument injections could be defined like this:
.. code-block:: python
factory = Factory(SomeClass,
'positional_arg1', 'positional_arg2',
keyword_argument1=3, keyword_argument=4)
# or
factory = Factory(SomeClass) \
.add_args('positional_arg1', 'positional_arg2') \
.add_kwargs(keyword_argument1=3, keyword_argument=4)
# or
factory = Factory(SomeClass)
factory.add_args('positional_arg1', 'positional_arg2')
factory.add_kwargs(keyword_argument1=3, keyword_argument=4)
Attribute injections are defined by using :py:meth:`Factory.attributes`:
.. code-block:: python
factory = Factory(SomeClass) \
.add_attributes(attribute1=1, attribute2=2)
Retrieving of provided instance can be performed via calling
:py:class:`Factory` object:
.. code-block:: python
factory = Factory(SomeClass)
some_object = factory()
.. py:attribute:: provided_type
If provided type is defined, provider checks that providing class is
its subclass.
:type: type | None
.. py:attribute:: cls
Class that provides object.
Alias for :py:attr:`provides`.
:type: type
"""
provided_type = None
__slots__ = ('cls', 'attributes')
def __init__(self, provides, *args, **kwargs):
"""Initializer.
:param provides: Class or other callable that provides object
for creation.
:type provides: type | callable
"""
if (self.__class__.provided_type and
not issubclass(provides, self.__class__.provided_type)):
raise Error('{0} can provide only {1} instances'.format(
self.__class__, self.__class__.provided_type))
self.attributes = dict()
super(Factory, self).__init__(provides, *args, **kwargs)
self.cls = self.provides
def add_attributes(self, **kwargs):
"""Add attribute injections.
:param kwargs: Dictionary of injections.
:type kwargs: dict
:return: Reference ``self``
"""
self.attributes.update(_parse_keyword_injections(kwargs))
return self
def _provide(self, *args, **kwargs):
"""Return provided instance.
:param args: Tuple of context positional arguments.
:type args: tuple[object]
:param kwargs: Dictionary of context keyword arguments.
:type kwargs: dict[str, object]
:rtype: object
"""
instance = super(Factory, self)._provide(*args, **kwargs)
for name, arg in six.iteritems(self.attributes):
setattr(instance, name, arg.provide_injection())
return instance
class DelegatedFactory(Factory):
""":py:class:`Factory` that is injected "as is".
.. py:attribute:: provided_type
If provided type is defined, provider checks that providing class is
its subclass.
:type: type | None
.. py:attribute:: cls
Class that provides object.
Alias for :py:attr:`provides`.
:type: type
"""
def provide_injection(self):
"""Injection strategy implementation.
:rtype: object
"""
return self
class Singleton(Factory):
""":py:class:`Singleton` provider returns same instance on every call.
:py:class:`Singleton` provider creates instance once and returns it on
every call. :py:class:`Singleton` extends :py:class:`Factory`, so, please
follow :py:class:`Factory` documentation for getting familiar with
injections syntax.
:py:class:`Singleton` is thread-safe and could be used in multithreading
environment without any negative impact.
Retrieving of provided instance can be performed via calling
:py:class:`Singleton` object:
.. code-block:: python
singleton = Singleton(SomeClass)
some_object = singleton()
.. py:attribute:: provided_type
If provided type is defined, provider checks that providing class is
its subclass.
:type: type | None
.. py:attribute:: cls
Class that provides object.
Alias for :py:attr:`provides`.
:type: type
"""
__slots__ = ('instance',)
def __init__(self, provides, *args, **kwargs):
"""Initializer.
:param provides: Class or other callable that provides object
for creation.
:type provides: type | callable
"""
self.instance = None
super(Singleton, self).__init__(provides, *args, **kwargs)
def reset(self):
"""Reset cached instance, if any.
:rtype: None
"""
self.instance = None
def _provide(self, *args, **kwargs):
"""Return provided instance.
:param args: Tuple of context positional arguments.
:type args: tuple[object]
:param kwargs: Dictionary of context keyword arguments.
:type kwargs: dict[str, object]
:rtype: object
"""
with GLOBAL_LOCK:
if self.instance is None:
self.instance = super(Singleton, self)._provide(*args,
**kwargs)
return self.instance
class DelegatedSingleton(Singleton):
""":py:class:`Singleton` that is injected "as is".
.. py:attribute:: provided_type
If provided type is defined, provider checks that providing class is
its subclass.
:type: type | None
.. py:attribute:: cls
Class that provides object.
Alias for :py:attr:`provides`.
:type: type
"""
def provide_injection(self):
"""Injection strategy implementation.
:rtype: object
"""
return self
class ThreadLocalSingleton(Factory):
""":py:class:`ThreadLocalSingleton` is singleton based on thread locals.
:py:class:`ThreadLocalSingleton` provider creates instance once for each
thread and returns it on every call. :py:class:`ThreadLocalSingleton`
extends :py:class:`Factory`, so, please follow :py:class:`Factory`
documentation for getting familiar with injections syntax.
:py:class:`ThreadLocalSingleton` is thread-safe and could be used in
multithreading environment without any negative impact.
Retrieving of provided instance can be performed via calling
:py:class:`ThreadLocalSingleton` object:
.. code-block:: python
singleton = ThreadLocalSingleton(SomeClass)
some_object = singleton()
.. py:attribute:: provided_type
If provided type is defined, provider checks that providing class is
its subclass.
:type: type | None
.. py:attribute:: cls
Class that provides object.
Alias for :py:attr:`provides`.
:type: type
"""
__slots__ = ('local_storage',)
def __init__(self, provides, *args, **kwargs):
"""Initializer.
:param provides: Class or other callable that provides object
for creation.
:type provides: type | callable
"""
self.local_storage = threading.local()
super(ThreadLocalSingleton, self).__init__(provides, *args, **kwargs)
def reset(self):
"""Reset cached instance, if any.
:rtype: None
"""
self.local_storage.instance = None
def _provide(self, *args, **kwargs):
"""Return provided instance.
:param args: Tuple of context positional arguments.
:type args: tuple[object]
:param kwargs: Dictionary of context keyword arguments.
:type kwargs: dict[str, object]
:rtype: object
"""
try:
instance = self.local_storage.instance
except AttributeError:
instance = super(ThreadLocalSingleton, self)._provide(*args,
**kwargs)
self.local_storage.instance = instance
finally:
return instance
class DelegatedThreadLocalSingleton(ThreadLocalSingleton):
""":py:class:`ThreadLocalSingleton` that is injected "as is".
.. py:attribute:: provided_type
If provided type is defined, provider checks that providing class is
its subclass.
:type: type | None
.. py:attribute:: cls
Class that provides object.
Alias for :py:attr:`provides`.
:type: type
"""
def provide_injection(self):
"""Injection strategy implementation.
:rtype: object
"""
return self

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@ -0,0 +1,113 @@
"""Dependency injector utils module."""
import sys
import copy as _copy
import types
import threading
import six
from dependency_injector import errors
GLOBAL_LOCK = threading.RLock()
"""Dependency injector global reentrant lock.
:type: :py:class:`threading.RLock`
"""
_IS_PYPY = '__pypy__' in sys.builtin_module_names
if _IS_PYPY or six.PY3: # pragma: no cover
_OBJECT_INIT = six.get_unbound_function(object.__init__)
else: # pragma: no cover
_OBJECT_INIT = None
if six.PY2: # pragma: no cover
_copy._deepcopy_dispatch[types.MethodType] = \
lambda obj, memo: type(obj)(obj.im_func,
_copy.deepcopy(obj.im_self, memo),
obj.im_class)
def is_provider(instance):
"""Check if instance is provider instance.
:param instance: Instance to be checked.
:type instance: object
:rtype: bool
"""
return (not isinstance(instance, six.class_types) and
hasattr(instance, '__IS_PROVIDER__') and
getattr(instance, '__IS_PROVIDER__') is True)
def ensure_is_provider(instance):
"""Check if instance is provider instance and return it.
:param instance: Instance to be checked.
:type instance: object
:raise: :py:exc:`dependency_injector.errors.Error` if provided instance is
not provider.
:rtype: :py:class:`dependency_injector.providers.Provider`
"""
if not is_provider(instance):
raise errors.Error('Expected provider instance, '
'got {0}'.format(str(instance)))
return instance
def is_container(instance):
"""Check if instance is container instance.
:param instance: Instance to be checked.
:type instance: object
:rtype: bool
"""
return (hasattr(instance, '__IS_CONTAINER__') and
getattr(instance, '__IS_CONTAINER__', False) is True)
def represent_provider(provider, provides):
"""Return string representation of provider.
:param provider: Provider object
:type provider: :py:class:`dependency_injector.providers.Provider`
:param provides: Object that provider provides
:type provider: object
:return: String representation of provider
:rtype: str
"""
return '<{provider}({provides}) at {address}>'.format(
provider='.'.join((provider.__class__.__module__,
provider.__class__.__name__)),
provides=repr(provides) if provides is not None else '',
address=hex(id(provider)))
def fetch_cls_init(cls):
"""Return reference to the class.__init__() method if it is defined.
:param cls: Class instance
:type cls: type
:return: Reference to the class.__init__() if any, or None otherwise.
:rtype: unbound method | None
"""
try:
cls_init = six.get_unbound_function(cls.__init__)
assert cls_init is not _OBJECT_INIT
except (AttributeError, AssertionError):
return None
else:
return cls_init
def deepcopy(instance, memo=None):
"""Make full copy of instance."""
return _copy.deepcopy(instance, memo)

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@ -1,9 +0,0 @@
.no-border {
border: 0 !important;
box-shadow: none !important;
-webkit-box-shadow: none !important;
}
.no-border td {
border: 0px !important;
padding: 0px 10px 0px 0px !important;
}

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@ -1 +0,0 @@
<iframe src="https://github.com/sponsors/rmk135/button" title="Sponsor Dependency Injector" height="32" width="114" style="border: 0; border-radius: 6px;"></iframe>

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@ -0,0 +1,70 @@
Advanced usage
==============
Current section of documentation describes advanced usage of
*Dependency Injector*.
@inject decorator
-----------------
.. currentmodule:: dependency_injector.injections
.. warning::
:py:func:`inject` decorator has been deprecated since version 2.2.0.
:py:func:`inject` decorator is a part of
:py:mod:`dependency_injector.injections` module.
:py:func:`inject` decorator can be used for making *inline* dependency
injections. It *patches* decorated callable in such way that dependency
injection will be done during every call of decorated callable.
:py:func:`inject` takes a various number of positional and keyword arguments
that are used as decorated callable injections. Every time, when
:py:func:`inject` is called, positional and keyword argument injections would
be passed as an callable arguments.
Such behaviour is very similar to the standard Python ``functools.partial``
object, except of one thing: all injectable values are provided
*"as is"*, except of providers (subclasses of
:py:class:`dependency_injector.providers.Provider`). Providers
will be called every time, when injection needs to be done. For example,
if injectable value of injection is a
:py:class:`dependency_injector.providers.Factory`, it will provide new one
instance (as a result of its call) every time, when injection needs to be done.
:py:func:`inject` behaviour with context positional and keyword arguments is
very like a standard Python ``functools.partial``:
- Positional context arguments will be appended after :py:func:`inject`
positional injections.
- Keyword context arguments have priority on :py:func:`inject` keyword
injections and will be merged over them.
Example:
.. literalinclude:: ../../examples/advanced_usage/inject_simple.py
:language: python
:linenos:
Example of usage :py:func:`inject` decorator with Flask:
.. literalinclude:: ../../examples/advanced_usage/inject_flask.py
:language: python
:linenos:
@inject decorator with classes
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
:py:func:`inject` could be applied for classes. In such case, it will look for
class ``__init__()`` method and pass injection to it. If decorated class has
no ``__init__()`` method, appropriate
:py:exc:`dependency_injector.errors.Error` will be raised.
Example of usage :py:func:`inject` with Flask class-based view:
.. literalinclude:: ../../examples/advanced_usage/inject_flask_class_based.py
:language: python
:linenos:

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@ -1,9 +1,6 @@
dependency_injector.containers
==============================
``dependency_injector.containers``
--------------------------------
.. automodule:: dependency_injector.containers
:members:
:inherited-members:
:show-inheritance:
.. disqus::
:special-members:

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@ -1,8 +1,5 @@
dependency_injector.errors
==========================
``dependency_injector.errors``
------------------------------
.. automodule:: dependency_injector.errors
:members:
.. disqus::

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@ -4,8 +4,8 @@ API Documentation
.. toctree::
:maxdepth: 2
top-level
providers
containers
wiring
errors
top_level
providers
containers
utils
errors

7
docs/api/injections.rst Normal file
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@ -0,0 +1,7 @@
``dependency_injector.injections``
----------------------------------
.. automodule:: dependency_injector.injections
:members:
:inherited-members:
:show-inheritance:

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@ -1,10 +1,11 @@
dependency_injector.providers
=============================
``dependency_injector.providers``
---------------------------------
.. image:: /images/providers/providers_class_diagram.png
:width: 100%
:align: center
.. automodule:: dependency_injector.providers
:members:
:inherited-members:
:show-inheritance:
.. disqus::

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@ -1,8 +0,0 @@
dependency_injector
===================
.. automodule:: dependency_injector
:members: __version__
.. disqus::

5
docs/api/top_level.rst Normal file
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@ -0,0 +1,5 @@
``dependency_injector``
-----------------------
.. automodule:: dependency_injector
:members: VERSION

5
docs/api/utils.rst Normal file
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@ -0,0 +1,5 @@
``dependency_injector.utils``
-----------------------------
.. automodule:: dependency_injector.utils
:members:

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@ -1,7 +0,0 @@
dependency_injector.wiring
=============================
.. automodule:: dependency_injector.wiring
:members:
.. disqus::

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@ -15,54 +15,49 @@
import os
import re
import sys
import alabaster
# If extensions (or modules to document with autodoc) are in another directory,
# add these directories to sys.path here. If the directory is relative to the
# documentation root, use os.path.abspath to make it absolute, like shown here.
sys.path.insert(0, os.path.abspath(".."))
sys.path.insert(0, os.path.abspath('..'))
# -- General configuration ------------------------------------------------
# If your documentation needs a minimal Sphinx version, state it here.
#needs_sphinx = "1.0"
#needs_sphinx = '1.0'
# Add any Sphinx extension module names here, as strings. They can be
# extensions coming with Sphinx (named "sphinx.ext.*") or your custom
# extensions coming with Sphinx (named 'sphinx.ext.*') or your custom
# ones.
extensions = [
"alabaster",
"sphinx.ext.autodoc",
"sphinx_disqus.disqus",
]
extensions = ['sphinx.ext.autodoc']
# Add any paths that contain templates here, relative to this directory.
templates_path = ["_templates"]
templates_path = ['_templates']
# The suffix(es) of source filenames.
# You can specify multiple suffix as a list of string:
# source_suffix = [".rst", ".md"]
source_suffix = ".rst"
# source_suffix = ['.rst', '.md']
source_suffix = '.rst'
# The encoding of source files.
#source_encoding = "utf-8-sig"
#source_encoding = 'utf-8-sig'
# The master toctree document.
master_doc = "index"
master_doc = 'index'
# General information about the project.
project = "Dependency Injector"
copyright = "2024, Roman Mogylatov"
author = "Roman Mogylatov"
project = u'Dependency Injector'
copyright = u'2016, ETS Labs'
author = u'ETS Labs'
# The version info for the project you"re documenting, acts as replacement for
# The version info for the project you're documenting, acts as replacement for
# |version| and |release|, also used in various other places throughout the
# built documents.
#
# The short X.Y version.
# Getting version:
with open("../src/dependency_injector/__init__.py") as init_file:
version = re.search("__version__ = \"(.*?)\"", init_file.read()).group(1)
with open('../dependency_injector/__init__.py') as init_file:
version = re.search('VERSION = \'(.*?)\'', init_file.read()).group(1)
# The full version, including alpha/beta/rc tags.
release = version
@ -76,19 +71,19 @@ language = None
# There are two options for replacing |today|: either, you set today to some
# non-false value, then it is used:
#today = ""
#today = ''
# Else, today_fmt is used as the format for a strftime call.
#today_fmt = "%B %d, %Y"
#today_fmt = '%B %d, %Y'
# List of patterns, relative to source directory, that match files and
# directories to ignore when looking for source files.
exclude_patterns = ["_build"]
exclude_patterns = ['_build']
# The reST default role (used for this markup: `text`) to use for all
# documents.
#default_role = None
# If true, "()" will be appended to :func: etc. cross-reference text.
# If true, '()' will be appended to :func: etc. cross-reference text.
#add_function_parentheses = True
# If true, the current module name will be prepended to all description
@ -100,7 +95,7 @@ exclude_patterns = ["_build"]
#show_authors = False
# The name of the Pygments (syntax highlighting) style to use.
pygments_style = "sphinx"
pygments_style = 'sphinx'
# A list of ignored prefixes for module index sorting.
#modindex_common_prefix = []
@ -116,16 +111,15 @@ todo_include_todos = False
# The theme to use for HTML and HTML Help pages. See the documentation for
# a list of builtin themes.
# html_theme = "sphinx_rtd_theme"
html_theme = "alabaster"
html_theme = 'default'
# Theme options are theme-specific and customize the look and feel of a theme
# further. For a list of options available for each theme, see the
# documentation.
# html_context = {}
#html_theme_options = {}
# Add any paths that contain custom themes here, relative to this directory.
html_theme_path = [alabaster.get_path()]
#html_theme_path = []
# The name for this set of Sphinx documents. If None, it defaults to
# "<project> v<release> documentation".
@ -141,24 +135,21 @@ html_theme_path = [alabaster.get_path()]
# The name of an image file (within the static path) to use as favicon of the
# docs. This file should be a Windows icon file (.ico) being 16x16 or 32x32
# pixels large.
html_favicon = "favicon.ico"
#html_favicon = None
# Add any paths that contain custom static files (such as style sheets) here,
# relative to this directory. They are copied after the builtin static files,
# so a file named "default.css" will overwrite the builtin "default.css".
html_static_path = ["_static"]
html_css_files = [
"custom.css",
]
html_static_path = []
# Add any extra paths that contain custom files (such as robots.txt or
# .htaccess) here, relative to this directory. These files are copied
# directly to the root of the documentation.
#html_extra_path = []
# If not "", a "Last updated on:" timestamp is inserted at every page bottom,
# If not '', a 'Last updated on:' timestamp is inserted at every page bottom,
# using the given strftime format.
#html_last_updated_fmt = "%b %d, %Y"
#html_last_updated_fmt = '%b %d, %Y'
# If true, SmartyPants will be used to convert quotes and dashes to
# typographically correct entities.
@ -192,50 +183,61 @@ html_css_files = [
# If true, an OpenSearch description file will be output, and all pages will
# contain a <link> tag referring to it. The value of this option must be the
# base URL from which the finished HTML is served.
#html_use_opensearch = ""
#html_use_opensearch = ''
# This is the file name suffix for HTML files (e.g. ".xhtml").
#html_file_suffix = None
# Language to be used for generating the HTML full-text search index.
# Sphinx supports the following languages:
# "da", "de", "en", "es", "fi", "fr", "hu", "it", "ja"
# "nl", "no", "pt", "ro", "ru", "sv", "tr"
#html_search_language = "en"
# 'da', 'de', 'en', 'es', 'fi', 'fr', 'hu', 'it', 'ja'
# 'nl', 'no', 'pt', 'ro', 'ru', 'sv', 'tr'
#html_search_language = 'en'
# A dictionary with options for the search language support, empty by default.
# Now only "ja" uses this config value
#html_search_options = {"type": "default"}
# Now only 'ja' uses this config value
#html_search_options = {'type': 'default'}
# The name of a javascript file (relative to the configuration directory) that
# implements a search results scorer. If empty, the default will be used.
#html_search_scorer = "scorer.js"
#html_search_scorer = 'scorer.js'
# Output file base name for HTML help builder.
htmlhelp_basename = "dependency_injectordoc"
htmlhelp_basename = 'dependency_injectordoc'
# on_rtd is whether we are on readthedocs.org, this line of code grabbed from
# docs.readthedocs.org
on_rtd = os.environ.get('READTHEDOCS', None) == 'True'
if not on_rtd: # only import and set the theme if we're building docs locally
import sphinx_rtd_theme
html_theme = 'sphinx_rtd_theme'
html_theme_path = [sphinx_rtd_theme.get_html_theme_path()]
# otherwise, readthedocs.org uses their theme by default, so no need to
# specify it
# -- Options for LaTeX output ---------------------------------------------
latex_elements = {
# The paper size ("letterpaper" or "a4paper").
#"papersize": "letterpaper",
# The paper size ('letterpaper' or 'a4paper').
#'papersize': 'letterpaper',
# The font size ("10pt", "11pt" or "12pt").
#"pointsize": "10pt",
# The font size ('10pt', '11pt' or '12pt').
#'pointsize': '10pt',
# Additional stuff for the LaTeX preamble.
#"preamble": "",
#'preamble': '',
# Latex figure (float) alignment
#"figure_align": "htbp",
#'figure_align': 'htbp',
}
# Grouping the document tree into LaTeX files. List of tuples
# (source start file, target name, title,
# author, documentclass [howto, manual, or own class]).
latex_documents = [
(master_doc, "dependency_injector.tex", u"Dependency Injector Documentation",
u"Roman Mogylatov", "manual"),
(master_doc, 'dependency_injector.tex', u'Dependency Injector Documentation',
u'ETS Labs', 'manual'),
]
# The name of an image file (relative to this directory) to place at the top of
@ -264,7 +266,7 @@ latex_documents = [
# One entry per manual page. List of tuples
# (source start file, name, description, authors, manual section).
man_pages = [
(master_doc, "Dependency Injector", u"Dependency Injector Documentation",
(master_doc, 'Dependency Injector', u'Dependency Injector Documentation',
[author], 1)
]
@ -278,9 +280,9 @@ man_pages = [
# (source start file, target name, title, author,
# dir menu entry, description, category)
texinfo_documents = [
(master_doc, "Dependency Injector", u"Dependency Injector Documentation",
author, "Dependency Injector", "Dependency injection microframework for Python",
"Miscellaneous"),
(master_doc, 'Dependency Injector', u'Dependency Injector Documentation',
author, 'Dependency Injector', 'Python dependency injection framework',
'Miscellaneous'),
]
# Documents to append as an appendix to all manuals.
@ -289,25 +291,10 @@ texinfo_documents = [
# If false, no module index is generated.
#texinfo_domain_indices = True
# How to display URL addresses: "footnote", "no", or "inline".
#texinfo_show_urls = "footnote"
# How to display URL addresses: 'footnote', 'no', or 'inline'.
#texinfo_show_urls = 'footnote'
# If true, do not generate a @detailmenu in the "Top" node"s menu.
# If true, do not generate a @detailmenu in the "Top" node's menu.
#texinfo_no_detailmenu = False
autodoc_member_order = "bysource"
disqus_shortname = "python-dependency-injector"
html_theme_options = {
"github_user": "ets-labs",
"github_repo": "python-dependency-injector",
"github_type": "star",
"github_button": True,
"github_banner": True,
"logo": "logo.svg",
"description": "Dependency injection framework for Python by Roman Mogylatov",
"code_font_size": "10pt",
"analytics_id": "UA-67012059-1",
"donate_url": "https://github.com/sponsors/rmk135",
}
autodoc_member_order = 'bysource'

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@ -1,18 +0,0 @@
.. _check-container-dependencies:
Check container dependencies
----------------------------
To check container dependencies use method ``.check_dependencies()``.
.. literalinclude:: ../../examples/containers/check_dependencies.py
:language: python
:lines: 3-
:emphasize-lines: 12
Method ``.check_dependencies()`` raises an error if container has any undefined dependencies.
If all dependencies are provided or have defaults, no error is raised.
See also: :ref:`dependency-provider`.
.. disqus::

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@ -1,23 +0,0 @@
Container copying
-----------------
You can create declarative container copies using ``@containers.copy()`` decorator.
.. literalinclude:: ../../examples/containers/declarative_copy_decorator1.py
:language: python
:lines: 3-
:emphasize-lines: 18-22
Decorator ``@containers.copy()`` copies providers from source container to destination container.
Destination container provider will replace source provider, if names match.
Decorator ``@containers.copy()`` helps you when you create derived declarative containers
from the base one. Base container often keeps default dependencies while derived containers define
overriding providers. Without ``@containers.copy()`` decorator, overridden providers are available
in the derived container, but base class dependencies continue to be bound to the base class providers.
.. literalinclude:: ../../examples/containers/declarative_copy_decorator2.py
:language: python
:lines: 11-
.. disqus::

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@ -1,72 +1,55 @@
Declarative container
---------------------
Declarative containers
----------------------
.. currentmodule:: dependency_injector.containers
:py:class:`DeclarativeContainer` is a class-based style of the providers definition.
:py:class:`DeclarativeContainer` is inversion of control container that
could be defined in declarative manner. It should cover most of the cases
when list of providers that would be included in container is deterministic
(container will not change its structure in runtime).
You create the declarative container subclass, put the providers as attributes and create the
container instance.
Declarative containers have to extend base declarative container class -
:py:class:`dependency_injector.containers.DeclarativeContainer`.
Declarative container's providers have to be defined like container's class
attributes. Every provider in container has name. This name should follow
``some_provider`` convention, that is standard naming convention for
attribute names in Python.
.. note::
Declarative containers have several features that could be useful
for some kind of operations on container's providers, please visit API
documentation for getting full list of features -
:py:class:`dependency_injector.containers.DeclarativeContainer`.
Here is an simple example of defining declarative container with several
factories:
.. image:: /images/containers/declarative.png
:width: 85%
:align: center
.. literalinclude:: ../../examples/containers/declarative.py
:language: python
:lines: 3-
:linenos:
The declarative container providers should only be used when you have the container instance.
Working with the providers of the container on the class level will influence all further
instances.
Example of declarative containers inheritance:
The declarative container can not have any methods or any other attributes then providers.
The container class provides next attributes:
- ``providers`` - the dictionary of all the container providers
- ``cls_providers`` - the dictionary of the container providers of the current container
- ``inherited_providers`` - the dictionary of all the inherited container providers
.. image:: /images/containers/declarative_inheritance.png
:width: 100%
:align: center
.. literalinclude:: ../../examples/containers/declarative_inheritance.py
:language: python
:lines: 3-
:linenos:
Injections in the declarative container are done the usual way:
Example of declarative containers's provider injections:
.. image:: /images/containers/declarative_injections.png
:width: 100%
:align: center
.. literalinclude:: ../../examples/containers/declarative_injections.py
:language: python
:lines: 3-
You can override container providers while creating a container instance:
.. literalinclude:: ../../examples/containers/declarative_override_providers.py
:language: python
:lines: 3-
:emphasize-lines: 13
Alternatively, you can call ``container.override_providers()`` method when the container instance
already exists:
.. code-block:: python
:emphasize-lines: 3
container = Container()
container.override_providers(foo=mock.Mock(Foo), bar=mock.Mock(Bar))
assert isinstance(container.foo(), mock.Mock)
assert isinstance(container.bar(), mock.Mock)
You can also use ``container.override_providers()`` with a context manager to reset
provided overriding after the context is closed:
.. code-block:: python
:emphasize-lines: 3
container = Container()
with container.override_providers(foo=mock.Mock(Foo), bar=mock.Mock(Bar)):
assert isinstance(container.foo(), mock.Mock)
assert isinstance(container.bar(), mock.Mock)
assert isinstance(container.foo(), Foo)
assert isinstance(container.bar(), Bar)
.. disqus::
:linenos:

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@ -1,25 +1,27 @@
Dynamic container
-----------------
Dynamic containers
------------------
.. currentmodule:: dependency_injector.containers
:py:class:`DynamicContainer` is a collection of the providers defined in the runtime.
:py:class:`DynamicContainer` is an inversion of control container with dynamic
structure. It should cover most of the cases when list of providers that
would be included in container is non-deterministic and depends on
application's flow or its configuration (container's structure could be
determined just after application will be started and will do some initial
work, like parsing list of container's providers from the configuration).
You create the dynamic container instance and put the providers as attributes.
While :py:class:`DeclarativeContainer` acts on class-level,
:py:class:`DynamicContainer` does the same on instance-level.
Here is an simple example of defining dynamic container with several factories:
.. literalinclude:: ../../examples/containers/dynamic.py
:language: python
:lines: 3-
:linenos:
The dynamic container is good for the case when your application structure depends on the
configuration file or some other source that you can reach only after application is already
running (database, api, etc).
In this example we use the configuration to fill in the dynamic container with the providers:
Next example demonstrates creation of dynamic container based on some
configuration:
.. literalinclude:: ../../examples/containers/dynamic_runtime_creation.py
:language: python
:lines: 3-
.. disqus::
:linenos:

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@ -1,18 +1,21 @@
.. _containers:
IoC Containers
==============
Containers
==========
Containers are collections of providers. Main purpose of containers is to group
providers.
Containers are collections of the providers.
There are, actually, several popular cases of containers usage:
There are several use cases how you can use containers:
+ Keeping all the providers in a single container (most common).
+ Grouping of the providers from the same architectural layer (for example,
+ Keeping all providers in a single container.
+ Grouping of providers from the same architectural layer (for example,
``Services``, ``Models`` and ``Forms`` containers).
+ Grouping of providers from the same functional groups (for example,
container ``Users``, that contains all functional parts of the ``users``
package).
container ``Users``, that contains all functional parts of ``Users``
component).
Also, for both of these and some other cases, it might be useful to attach
some init / shutdown functionality or something else, that deals with group
of providers.
Containers module API docs - :py:mod:`dependency_injector.containers`.
@ -23,7 +26,3 @@ Containers module API docs - :py:mod:`dependency_injector.containers`.
dynamic
specialization
overriding
copying
reset_singletons
check_dependencies
traversal

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@ -1,40 +1,40 @@
Container overriding
--------------------
Overriding of containers
------------------------
.. currentmodule:: dependency_injector.containers
The container can be overridden by the other container. All of the providers from the overriding
container will override the providers with the same names in the overridden container.
Containers can be overridden by other containers. This, actually, means that
all of the providers from overriding container will override providers with
the same names in overridden container.
.. literalinclude:: ../../examples/containers/override.py
There are two ways to override :py:class:`DeclarativeContainer` with another
container:
- Use :py:meth:`DeclarativeContainer.override` method.
- Use :py:func:`override` class decorator.
Example of overriding container using :py:meth:`DeclarativeContainer.override`
method:
.. literalinclude:: ../../examples/containers/override_declarative.py
:language: python
:lines: 3-
:linenos:
It helps in a testing. Also you can use it for configuring project for the different
environments: replace an API client with a stub on the dev or stage.
Example of overriding container using :py:func:`override` decorator:
The container also has:
- ``container.overridden`` - tuple of all overriding containers.
- ``container.reset_last_overriding()`` - reset last overriding for each provider in the container.
- ``container.reset_override()`` - reset all overriding in the container.
:py:class:`DynamicContainer` has the same functionality.
Another possible way to override container providers on declarative level is
``@containers.override()`` decorator:
.. literalinclude:: ../../examples/containers/declarative_override_decorator.py
.. literalinclude:: ../../examples/containers/override_declarative_decorator.py
:language: python
:lines: 3-
:emphasize-lines: 12-16
:linenos:
Decorator ``@containers.override()`` takes a container for overriding as an argument.
This container providers will be overridden by the providers with the same names from
the decorated container.
Also there are several useful :py:class:`DeclarativeContainer` methods and
properties that help to work with container overridings:
It helps to change the behaviour of application by importing extension modules but not a code change.
Imported module can override providers in main container. While the code uses main container as
before, the overridden providers provide components defined in the extension module.
- :py:attr:`DeclarativeContainer.overridden` - tuple of all overriding
containers.
- :py:meth:`DeclarativeContainer.reset_last_overriding()` - reset last
overriding provider for each container providers.
- :py:meth:`DeclarativeContainer.reset_override()` - reset all overridings
for each container providers.
.. disqus::
:py:class:`DynamicContainer` has exactly the same functionality, except of
:py:func:`override` decorator.

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@ -1,31 +0,0 @@
.. _reset-container-singletons:
Reset container singletons
--------------------------
To reset all container singletons use method ``.reset_singletons()``.
.. literalinclude:: ../../examples/containers/reset_singletons.py
:language: python
:lines: 3-
:emphasize-lines: 16
Method ``.reset_singletons()`` also resets singletons in sub-containers: ``providers.Container`` and
``providers.DependenciesContainer.``
.. literalinclude:: ../../examples/containers/reset_singletons_subcontainers.py
:language: python
:lines: 3-
:emphasize-lines: 21
You can use ``.reset_singletons()`` method with a context manager. Singletons will be reset on
both entering and exiting a context.
.. literalinclude:: ../../examples/containers/reset_singletons_with.py
:language: python
:lines: 3-
:emphasize-lines: 14-15
See also: :ref:`singleton-provider`.
.. disqus::

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@ -1,25 +1,24 @@
Specialization of the container provider type
---------------------------------------------
Specialization of containers
----------------------------
.. currentmodule:: dependency_injector.containers
You can make a restriction of the :py:class:`DeclarativeContainer` provider type:
:py:class:`DeclarativeContainer` could be specialized for any kind of needs
via declaring its subclasses.
One of such `builtin` features is a limitation for providers type.
Next example shows usage of this feature with :py:class:`DeclarativeContainer`
in couple with feature of :py:class:`dependency_injector.providers.Factory`
for limitation of its provided type:
.. literalinclude:: ../../examples/containers/declarative_provider_type.py
:language: python
:lines: 3-
:emphasize-lines: 29-31
:linenos:
The emphasized lines will cause an error because ``other_provider`` is not a subtype of the
``ServiceProvider``. This helps to control the content of the container.
The same works for the :py:class:`DynamicContainer`:
Limitation for providers type could be used with :py:class:`DynamicContainer`
as well:
.. literalinclude:: ../../examples/containers/dynamic_provider_type.py
:language: python
:lines: 3-
:emphasize-lines: 23
The emphasized line will also cause an error.
.. disqus::
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@ -1,33 +0,0 @@
Container providers traversal
-----------------------------
To traverse container providers use method ``.traverse()``.
.. literalinclude:: ../../examples/containers/traverse.py
:language: python
:lines: 3-
:emphasize-lines: 38
Method ``.traverse()`` returns a generator. Traversal generator visits all container providers.
This includes nested providers even if they are not present on the root level of the container.
Traversal generator guarantees that each container provider will be visited only once.
It can traverse cyclic provider graphs.
Traversal generator does not guarantee traversal order.
You can use ``types=[...]`` argument to filter providers. Traversal generator will only return
providers matching specified types.
.. code-block:: python
:emphasize-lines: 3
container = Container()
for provider in container.traverse(types=[providers.Resource]):
print(provider)
# <dependency_injector.providers.Resource(<function init_database at 0x10bd2cb80>) at 0x10d346b40>
# <dependency_injector.providers.Resource(<function init_cache at 0x10be373a0>) at 0x10d346bc0>
.. disqus::

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@ -1,68 +0,0 @@
Chained Factories pattern
=========================
This example demonstrates "Chained Factories" pattern.
The idea of the pattern is in wrapping ``Factory`` into another ``Factory`` that adds
additional arguments.
.. code-block:: python
base_factory = providers.Factory(
SomeClass,
base_argument=1,
)
concrete_factory = providers.Factory(
base_factory,
extra_argument=2,
)
if __name__ == "__main__":
instance = concrete_factory()
# Same as: # instance = SomeClass(base_argument=1, extra_argument=2)
Sample code
-----------
Listing of the pattern example:
.. literalinclude:: ../../examples/miniapps/factory-patterns/chained_factories.py
:language: python
Arguments priority
------------------
Passing of the arguments works the same way like for any other :ref:`factory-provider`.
.. code-block:: python
# 1. Keyword arguments of upper level factory are added to lower level factory
chained_dict_factory = providers.Factory(
providers.Factory(dict, arg1=1),
arg2=2,
)
print(chained_dict_factory()) # prints: {"arg1": 1, "arg2": 2}
# 2. Keyword arguments of upper level factory have priority
chained_dict_factory = providers.Factory(
providers.Factory(dict, arg1=1),
arg1=2,
)
print(chained_dict_factory()) # prints: {"arg1": 2}
# 3. Keyword arguments provided from context have the most priority
chained_dict_factory = providers.Factory(
providers.Factory(dict, arg1=1),
arg1=2,
)
print(chained_dict_factory(arg1=3)) # prints: {"arg1": 3}
Credits
-------
The "Chained Factories" pattern was suggested by the ``Dependency Injector`` users.
.. disqus::

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@ -1,74 +0,0 @@
Factory of Factories pattern
============================
This example demonstrates "Factory of Factories" pattern.
The idea of the pattern is in creating a ``Factory`` that creates another ``Factory`` and adds
additional arguments.
.. code-block:: python
base_factory = providers.Factory(
providers.Factory
SomeClass,
base_argument=1,
)
concrete_factory = providers.Factory(
OtherClass,
instance=base_factory(extra_argument=1),
)
if __name__ == "__main__":
instance = concrete_factory()
# Same as: # instance = SomeClass(base_argument=1, extra_argument=2)
Sample code
-----------
Listing of the pattern example:
.. literalinclude:: ../../examples/miniapps/factory-patterns/factory_of_factories.py
:language: python
Arguments priority
------------------
Passing of the arguments works the same way like for any other :ref:`factory-provider`.
.. code-block:: python
# 1. Keyword arguments of upper level factory are added to lower level factory
factory_of_dict_factories = providers.Factory(
providers.Factory,
dict,
arg1=1,
)
dict_factory = factory_of_dict_factories(arg2=2)
print(dict_factory()) # prints: {"arg1": 1, "arg2": 2}
# 2. Keyword arguments of upper level factory have priority
factory_of_dict_factories = providers.Factory(
providers.Factory,
dict,
arg1=1,
)
dict_factory = factory_of_dict_factories(arg1=2)
print(dict_factory()) # prints: {"arg1": 2}
# 3. Keyword arguments provided from context have the most priority
factory_of_dict_factories = providers.Factory(
providers.Factory,
dict,
arg1=1,
)
dict_factory = factory_of_dict_factories(arg1=2)
print(dict_factory(arg1=3)) # prints: {"arg1": 3}
Credits
-------
The "Factory of Factories" pattern was suggested by the ``Dependency Injector`` users.
.. disqus::

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@ -1,17 +0,0 @@
Other examples
==============
.. meta::
:keywords: Python,Dependency Injection,Inversion of Control,Container,Example,Application,
Framework
:description: This sections contains assorted Dependency Injector examples.
This sections contains assorted ``Dependency Injector`` examples.
.. toctree::
:maxdepth: 2
use-cases
password-hashing
chained-factories
factory-of-factories

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@ -1,30 +0,0 @@
Password hashing example
========================
.. meta::
:keywords: Python,Dependency Injection,Inversion of Control,Container,Example,Application,
Framework,Callable
:description: This example demonstrates a usage of the Callable provider.
This example demonstrates an injection of the ``Callable`` provider.
The source code is available on the `Github <https://github.com/ets-labs/python-dependency-injector/tree/master/examples/miniapps/password-hashing>`_.
Sample code
-----------
Listing of the pattern example:
.. literalinclude:: ../../examples/miniapps/password-hashing/example.py
:language: python
Run the example
---------------
Instructions for running:
.. code-block:: bash
python example.py
.. disqus::

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@ -1,74 +0,0 @@
Use cases example
=================
.. meta::
:keywords: Python,Dependency Injection,Inversion of Control,Container,Example,Application,
Framework,DependenciesContainer
:description: This example demonstrates a usage of the DependenciesContainer provider.
This example demonstrates a usage of the ``DependenciesContainer`` provider.
The source code is available on the `Github <https://github.com/ets-labs/python-dependency-injector/tree/master/examples/miniapps/decoupled-packages>`_.
Application structure
---------------------
Example application has next structure:
.. code-block:: bash
./
└── example/
├── __init__.py
├── __main__.py
├── adapters.py
├── containers.py
└── usecases.py
Containers
----------
Listing of the ``example/containers.py``:
.. literalinclude:: ../../examples/miniapps/use-cases/example/containers.py
:language: python
Main module
-----------
Listing of the ``example/__main__.py``:
.. literalinclude:: ../../examples/miniapps/use-cases/example/__main__.py
:language: python
Run the application
-------------------
Instructions for running in the "test" mode:
.. code-block:: bash
python run.py test example@example.com
Instructions for running in the "prod" mode:
.. code-block:: bash
python run.py prod example@example.com
Adapters and use cases
----------------------
Listing of the ``example/adapters.py``:
.. literalinclude:: ../../examples/miniapps/use-cases/example/adapters.py
:language: python
Listing of the ``example/usecases.py``:
.. literalinclude:: ../../examples/miniapps/use-cases/example/usecases.py
:language: python
.. disqus::

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@ -1,83 +0,0 @@
.. _aiohttp-example:
Aiohttp example
===============
.. meta::
:keywords: Python,Dependency Injection,Aiohttp,Example
:description: This example demonstrates a usage of the Aiohttp and Dependency Injector.
This example shows how to use ``Dependency Injector`` with `Aiohttp <https://docs.aiohttp.org/>`_.
The example application is a REST API that searches for funny GIFs on the `Giphy <https://giphy.com/>`_.
The source code is available on the `Github <https://github.com/ets-labs/python-dependency-injector/tree/master/examples/miniapps/aiohttp>`_.
:ref:`aiohttp-tutorial` demonstrates how to build this application step-by-step.
Application structure
---------------------
Application has next structure:
.. code-block:: bash
./
├── giphynavigator/
│ ├── __init__.py
│ ├── application.py
│ ├── containers.py
│ ├── giphy.py
│ ├── handlers.py
│ ├── services.py
│ └── tests.py
├── config.yml
└── requirements.txt
Container
---------
Declarative container is defined in ``giphynavigator/containers.py``:
.. literalinclude:: ../../examples/miniapps/aiohttp/giphynavigator/containers.py
:language: python
Handlers
--------
Handler has dependencies on search service and some config options. The dependencies are injected
using :ref:`wiring` feature.
Listing of ``giphynavigator/handlers.py``:
.. literalinclude:: ../../examples/miniapps/aiohttp/giphynavigator/handlers.py
:language: python
Application factory
-------------------
Application factory creates container, wires it with the ``handlers`` module, creates
``Aiohttp`` app and setup routes.
Listing of ``giphynavigator/application.py``:
.. literalinclude:: ../../examples/miniapps/aiohttp/giphynavigator/application.py
:language: python
Tests
-----
Tests use :ref:`provider-overriding` feature to replace giphy client with a mock ``giphynavigator/tests.py``:
.. literalinclude:: ../../examples/miniapps/aiohttp/giphynavigator/tests.py
:language: python
:emphasize-lines: 32,59,73
Sources
-------
Explore the sources on the `Github <https://github.com/ets-labs/python-dependency-injector/tree/master/examples/miniapps/aiohttp>`_.
.. include:: ../sponsor.rst
.. disqus::

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@ -1,89 +0,0 @@
.. _application-multiple-containers:
Application example (multiple containers)
=========================================
.. meta::
:keywords: Python,Dependency Injection,Inversion of Control,Container,Example,Application,
Framework,AWS,boto3,client
:description: This example shows how you can create an application using multiple declarative
containers. We build an example Python micro application following the dependency
injection principle. It consists from several services with a domain logic that
have dependencies on database & AWS S3.
This example shows how you can create an application using multiple declarative containers. Using
multiple declarative containers is a good choice for a large application. For
building a moderate or a small size application refer to :ref:`application-single-container`.
We build an example micro application following the dependency injection principle. It consists
of several services with a domain logic. The services have dependencies on database & AWS S3.
.. image:: images/application.png
:width: 100%
:align: center
Start from the scratch or jump to the section:
.. contents::
:local:
:backlinks: none
You can find the source code and instructions for running on the `Github <https://github.com/ets-labs/python-dependency-injector/tree/master/examples/miniapps/application-multiple-containers>`_.
Application structure
---------------------
Application consists of an ``example`` package, a configuration file and a ``requirements.txt``
file.
.. code-block:: bash
./
├── example/
│ ├── __init__.py
│ ├── __main__.py
│ ├── containers.py
│ └── services.py
├── config.yml
└── requirements.txt
Containers
----------
Listing of the ``example/containers.py``:
.. literalinclude:: ../../examples/miniapps/application-multiple-containers/example/containers.py
:language: python
Main module
-----------
Listing of the ``example/__main__.py``:
.. literalinclude:: ../../examples/miniapps/application-multiple-containers/example/__main__.py
:language: python
Services
--------
Listing of the ``example/services.py``:
.. literalinclude:: ../../examples/miniapps/application-multiple-containers/example/services.py
:language: python
Configuration
-------------
Listing of the ``config.yml``:
.. literalinclude:: ../../examples/miniapps/application-multiple-containers/config.yml
:language: yaml
Run the application
-------------------
You can find the source code and instructions for running on the `Github <https://github.com/ets-labs/python-dependency-injector/tree/master/examples/miniapps/application-multiple-containers>`_.
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.. _application-single-container:
Application example (single container)
======================================
.. meta::
:keywords: Python,Dependency Injection,Inversion of Control,Container,Example,Application,
Framework,AWS,boto3,client
:description: This example shows how you can create an application using a single declarative
container. We build an example Python micro application following the dependency
injection principle. It consists from several services with a domain logic that
have dependencies on database & AWS S3.
This example shows how you can create an application using a single declarative container. Using
a single declarative container is a good choice for small or moderate size application. For
building a large application refer to :ref:`application-multiple-containers`.
We build an example micro application following the dependency injection principle. It consists
of several services with a domain logic. The services have dependencies on database & AWS S3.
.. image:: images/application.png
:width: 100%
:align: center
Start from the scratch or jump to the section:
.. contents::
:local:
:backlinks: none
You can find the source code and instructions for running on the `Github <https://github.com/ets-labs/python-dependency-injector/tree/master/examples/miniapps/application-single-container>`_.
Application structure
---------------------
Application consists of an ``example`` package, several configuration files and a
``requirements.txt`` file.
.. code-block:: bash
./
├── example/
│ ├── __init__.py
│ ├── __main__.py
│ ├── containers.py
│ └── services.py
├── config.ini
├── logging.ini
└── requirements.txt
Container
---------
Listing of the ``example/containers.py``:
.. literalinclude:: ../../examples/miniapps/application-single-container/example/containers.py
:language: python
Main module
-----------
Listing of the ``example/__main__.py``:
.. literalinclude:: ../../examples/miniapps/application-single-container/example/__main__.py
:language: python
Services
--------
Listing of the ``example/services.py``:
.. literalinclude:: ../../examples/miniapps/application-single-container/example/services.py
:language: python
Configuration
-------------
Listing of the ``config.ini``:
.. literalinclude:: ../../examples/miniapps/application-single-container/config.ini
:language: ini
Listing of the ``logging.ini``:
.. literalinclude:: ../../examples/miniapps/application-single-container/logging.ini
:language: ini
Run the application
-------------------
You can find the source code and instructions for running on the `Github <https://github.com/ets-labs/python-dependency-injector/tree/master/examples/miniapps/application-single-container>`_.
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.. _boto3-example:
Boto3 example
=============
.. meta::
:keywords: Python,Dependency Injection,Boto3,AWS,Amazon Web Services,S3,SQS,Rout53,EC2,Lambda,Example
:description: This example demonstrates a usage of Boto3 AWS client and Dependency Injector.
This example shows how to use ``Dependency Injector`` with `Boto3 <https://github.com/boto/boto3>`_.
The source code is available on the `Github <https://github.com/ets-labs/python-dependency-injector/tree/master/examples/miniapps/boto3-session>`_.
Listing of ``boto3_session_example.py``:
.. literalinclude:: ../../examples/miniapps/boto3-session/boto3_session_example.py
:language: python
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.. _decoupled-packages:
Decoupled packages example (multiple containers)
================================================
.. meta::
:keywords: Python,Dependency Injection,Inversion of Control,Container,Example,Application,
Framework,AWS,boto3,client
:description: This example shows how to use Dependency Injector to create Python decoupled packages.
To achieve a decoupling each package has a container with the components. When
a component needs a dependency from the outside of the package scope we use the
Dependency provider. The package container has no knowledge on where the
dependencies come from. It states a need that the dependencies must be provided.
This helps to decouple a package from the 3rd party dependencies and other
packages.
This example shows how to use ``Dependency Injector`` to create decoupled packages.
To achieve a decoupling each package has a container with the components. When a component needs a
dependency from the outside of the package scope we use the ``Dependency`` provider. The package
container has no knowledge on where the dependencies come from. It states a need that the
dependencies must be provided. This helps to decouple a package from the 3rd party dependencies
and other packages.
To wire the packages we use an application container. Application container has all 3rd party
dependencies and package containers. It wires the packages and dependencies to create a
complete application.
We build an example micro application that consists of 3 packages:
- ``user`` - a package with user domain logic, depends on a database
- ``photo`` - a package with photo domain logic, depends on a database and AWS S3
- ``analytics`` - a package with analytics domain logic, depends on the ``user`` and ``photo``
package components
.. image:: images/decoupled-packages.png
:width: 100%
:align: center
Start from the scratch or jump to the section:
.. contents::
:local:
:backlinks: none
You can find the source code and instructions for running on the `Github <https://github.com/ets-labs/python-dependency-injector/tree/master/examples/miniapps/decoupled-packages>`_.
Application structure
---------------------
Application consists of an ``example`` package, a configuration file and a ``requirements.txt``
file.
.. code-block:: bash
./
├── example/
│ ├── analytics/
│ │ ├── __init__.py
│ │ ├── containers.py
│ │ └── services.py
│ ├── photo/
│ │ ├── __init__.py
│ │ ├── containers.py
│ │ ├── entities.py
│ │ └── repositories.py
│ ├── user/
│ │ ├── __init__.py
│ │ ├── containers.py
│ │ ├── entities.py
│ │ └── repositories.py
│ ├── __init__.py
│ ├── __main__.py
│ └── containers.py
├── config.ini
└── requirements.txt
Package containers
------------------
Listing of the ``example/user/containers.py``:
.. literalinclude:: ../../examples/miniapps/decoupled-packages/example/user/containers.py
:language: python
Listing of the ``example/photo/containers.py``:
.. literalinclude:: ../../examples/miniapps/decoupled-packages/example/photo/containers.py
:language: python
Listing of the ``example/analytics/containers.py``:
.. literalinclude:: ../../examples/miniapps/decoupled-packages/example/analytics/containers.py
:language: python
Application container
---------------------
Application container consists of all packages and 3rd party dependencies. Its role is to wire
everything together in a complete application.
Listing of the ``example/containers.py``:
.. literalinclude:: ../../examples/miniapps/decoupled-packages/example/containers.py
:language: python
.. note::
Package ``analytics`` has dependencies on the repositories from the ``user`` and
``photo`` packages. This is an example of how you can pass the dependencies from one package
to another.
Main module
-----------
Listing of the ``example/__main__.py``:
.. literalinclude:: ../../examples/miniapps/decoupled-packages/example/__main__.py
:language: python
Configuration
-------------
Listing of the ``config.ini``:
.. literalinclude:: ../../examples/miniapps/decoupled-packages/config.ini
:language: ini
Run the application
-------------------
You can find the source code and instructions for running on the `Github <https://github.com/ets-labs/python-dependency-injector/tree/master/examples/miniapps/decoupled-packages>`_.
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.. _django-example:
Django example
==============
.. meta::
:keywords: Python,Dependency Injection,Django,Example
:description: This example demonstrates a usage of the Django and Dependency Injector.
This example shows how to use ``Dependency Injector`` with `Django <https://www.djangoproject.com/>`_.
The example application helps to search for repositories on the Github.
.. image:: images/django.png
:width: 100%
:align: center
The source code is available on the `Github <https://github.com/ets-labs/python-dependency-injector/tree/master/examples/miniapps/django>`_.
Application structure
---------------------
Application has standard Django project structure. It consists of ``githubnavigator`` project package and
``web`` application package:
.. code-block:: bash
./
├── githubnavigator/
│ ├── __init__.py
│ ├── asgi.py
│ ├── containers.py
│ ├── services.py
│ ├── settings.py
│ ├── urls.py
│ └── wsgi.py
├── web/
│ ├── templates/
│ │ ├── base.html
│ │ └── index.html
│ ├── __init__.py
│ ├── apps.py
│ ├── tests.py
│ ├── urls.py
│ └── views.py
├── manage.py
└── requirements.txt
Container
---------
Declarative container is defined in ``githubnavigator/containers.py``:
.. literalinclude:: ../../examples/miniapps/django/githubnavigator/containers.py
:language: python
Container instance is created in ``githubnavigator/__init__.py``:
.. literalinclude:: ../../examples/miniapps/django/githubnavigator/__init__.py
:language: python
Views
-----
View has dependencies on search service and some config options. The dependencies are injected
using :ref:`wiring` feature.
Listing of ``web/views.py``:
.. literalinclude:: ../../examples/miniapps/django/web/views.py
:language: python
App config
----------
Container is wired to the ``views`` module in the app config ``web/apps.py``:
.. literalinclude:: ../../examples/miniapps/django/web/apps.py
:language: python
:emphasize-lines: 13
Tests
-----
Tests use :ref:`provider-overriding` feature to replace github client with a mock ``web/tests.py``:
.. literalinclude:: ../../examples/miniapps/django/web/tests.py
:language: python
:emphasize-lines: 39,60
Sources
-------
Explore the sources on the `Github <https://github.com/ets-labs/python-dependency-injector/tree/master/examples/miniapps/django>`_.
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.. _fastapi-redis-example:
FastAPI + Redis example
=======================
.. meta::
:keywords: Python,Dependency Injection,FastAPI,Redis,Example
:description: This example demonstrates a usage of the FastAPI, Redis, and Dependency Injector.
This example shows how to use ``Dependency Injector`` with `FastAPI <https://fastapi.tiangolo.com/>`_ and
`Redis <https://redis.io/>`_.
The source code is available on the `Github <https://github.com/ets-labs/python-dependency-injector/tree/master/examples/miniapps/fastapi-redis>`_.
See also:
- Provider :ref:`async-injections`
- Resource provider :ref:`resource-async-initializers`
- Wiring :ref:`async-injections-wiring`
Application structure
---------------------
Application has next structure:
.. code-block:: bash
./
├── fastapiredis/
│ ├── __init__.py
│ ├── application.py
│ ├── containers.py
│ ├── redis.py
│ ├── services.py
│ └── tests.py
├── docker-compose.yml
├── Dockerfile
└── requirements.txt
Redis
-----
Module ``redis`` defines Redis connection pool initialization and shutdown. See ``fastapiredis/redis.py``:
.. literalinclude:: ../../examples/miniapps/fastapi-redis/fastapiredis/redis.py
:language: python
Service
-------
Module ``services`` contains example service. Service has a dependency on Redis connection pool.
It uses it for getting and setting a key asynchronously. Real life service will do something more meaningful.
See ``fastapiredis/services.py``:
.. literalinclude:: ../../examples/miniapps/fastapi-redis/fastapiredis/services.py
:language: python
Container
---------
Declarative container wires example service with Redis connection pool. See ``fastapiredis/containers.py``:
.. literalinclude:: ../../examples/miniapps/fastapi-redis/fastapiredis/containers.py
:language: python
Application
-----------
Module ``application`` creates ``FastAPI`` app, setup endpoint, and init container.
Endpoint ``index`` has a dependency on example service. The dependency is injected using :ref:`wiring` feature.
Listing of ``fastapiredis/application.py``:
.. literalinclude:: ../../examples/miniapps/fastapi-redis/fastapiredis/application.py
:language: python
Tests
-----
Tests use :ref:`provider-overriding` feature to replace example service with a mock. See ``fastapiredis/tests.py``:
.. literalinclude:: ../../examples/miniapps/fastapi-redis/fastapiredis/tests.py
:language: python
:emphasize-lines: 24
Sources
-------
The source code is available on the `Github <https://github.com/ets-labs/python-dependency-injector/tree/master/examples/miniapps/fastapi-redis>`_.
See also:
- Provider :ref:`async-injections`
- Resource provider :ref:`resource-async-initializers`
- Wiring :ref:`async-injections-wiring`
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.. _fastapi-sqlalchemy-example:
FastAPI + SQLAlchemy example
============================
.. meta::
:keywords: Python,Dependency Injection,FastAPI,SQLAlchemy,Example
:description: This example demonstrates a usage of the FastAPI, SQLAlchemy, and Dependency Injector.
This example shows how to use ``Dependency Injector`` with `FastAPI <https://fastapi.tiangolo.com/>`_ and
`SQLAlchemy <https://www.sqlalchemy.org/>`_.
The source code is available on the `Github <https://github.com/ets-labs/python-dependency-injector/tree/master/examples/miniapps/fastapi-sqlalchemy>`_.
Thanks to `@ShvetsovYura <https://github.com/ShvetsovYura>`_ for providing initial example:
`FastAPI_DI_SqlAlchemy <https://github.com/ShvetsovYura/FastAPI_DI_SqlAlchemy>`_.
Application structure
---------------------
Application has next structure:
.. code-block:: bash
./
├── webapp/
│ ├── __init__.py
│ ├── application.py
│ ├── containers.py
│ ├── database.py
│ ├── endpoints.py
│ ├── models.py
│ ├── repositories.py
│ ├── services.py
│ └── tests.py
├── config.yml
├── docker-compose.yml
├── Dockerfile
└── requirements.txt
Application factory
-------------------
Application factory creates container, wires it with the ``endpoints`` module, creates
``FastAPI`` app, and setup routes.
Application factory also creates database if it does not exist.
Listing of ``webapp/application.py``:
.. literalinclude:: ../../examples/miniapps/fastapi-sqlalchemy/webapp/application.py
:language: python
Endpoints
---------
Module ``endpoints`` contains example endpoints. Endpoints have a dependency on user service.
User service is injected using :ref:`wiring` feature. See ``webapp/endpoints.py``:
.. literalinclude:: ../../examples/miniapps/fastapi-sqlalchemy/webapp/endpoints.py
:language: python
Container
---------
Declarative container wires example user service, user repository, and utility database class.
See ``webapp/containers.py``:
.. literalinclude:: ../../examples/miniapps/fastapi-sqlalchemy/webapp/containers.py
:language: python
Services
--------
Module ``services`` contains example user service. See ``webapp/services.py``:
.. literalinclude:: ../../examples/miniapps/fastapi-sqlalchemy/webapp/services.py
:language: python
Repositories
------------
Module ``repositories`` contains example user repository. See ``webapp/repositories.py``:
.. literalinclude:: ../../examples/miniapps/fastapi-sqlalchemy/webapp/repositories.py
:language: python
Models
------
Module ``models`` contains example SQLAlchemy user model. See ``webapp/models.py``:
.. literalinclude:: ../../examples/miniapps/fastapi-sqlalchemy/webapp/models.py
:language: python
Database
--------
Module ``database`` defines declarative base and utility class with engine and session factory.
See ``webapp/database.py``:
.. literalinclude:: ../../examples/miniapps/fastapi-sqlalchemy/webapp/database.py
:language: python
Tests
-----
Tests use :ref:`provider-overriding` feature to replace repository with a mock. See ``webapp/tests.py``:
.. literalinclude:: ../../examples/miniapps/fastapi-sqlalchemy/webapp/tests.py
:language: python
:emphasize-lines: 25, 45, 58, 74, 86, 97
Sources
-------
The source code is available on the `Github <https://github.com/ets-labs/python-dependency-injector/tree/master/examples/miniapps/fastapi-sqlalchemy>`_.
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.. _fastapi-example:
FastAPI example
===============
.. meta::
:keywords: Python,Dependency Injection,FastAPI,Example
:description: This example demonstrates a usage of the FastAPI and Dependency Injector.
This example shows how to use ``Dependency Injector`` with `FastAPI <https://fastapi.tiangolo.com/>`_.
The example application is a REST API that searches for funny GIFs on the `Giphy <https://giphy.com/>`_.
The source code is available on the `Github <https://github.com/ets-labs/python-dependency-injector/tree/master/examples/miniapps/fastapi>`_.
Application structure
---------------------
Application has next structure:
.. code-block:: bash
./
├── giphynavigator/
│ ├── __init__.py
│ ├── application.py
│ ├── containers.py
│ ├── endpoints.py
│ ├── giphy.py
│ ├── services.py
│ └── tests.py
├── config.yml
└── requirements.txt
Container
---------
Declarative container is defined in ``giphynavigator/containers.py``:
.. literalinclude:: ../../examples/miniapps/fastapi/giphynavigator/containers.py
:language: python
Endpoints
---------
Endpoint has a dependency on search service. There are also some config options that are used as default values.
The dependencies are injected using :ref:`wiring` feature.
Listing of ``giphynavigator/endpoints.py``:
.. literalinclude:: ../../examples/miniapps/fastapi/giphynavigator/endpoints.py
:language: python
Application factory
-------------------
Application factory creates container, wires it with the ``endpoints`` module, creates
``FastAPI`` app, and setup routes.
Listing of ``giphynavigator/application.py``:
.. literalinclude:: ../../examples/miniapps/fastapi/giphynavigator/application.py
:language: python
Tests
-----
Tests use :ref:`provider-overriding` feature to replace giphy client with a mock ``giphynavigator/tests.py``:
.. literalinclude:: ../../examples/miniapps/fastapi/giphynavigator/tests.py
:language: python
:emphasize-lines: 29,57,72
Sources
-------
Explore the sources on the `Github <https://github.com/ets-labs/python-dependency-injector/tree/master/examples/miniapps/fastapi>`_.
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.. _flask-blueprints-example:
Flask blueprints example
========================
.. meta::
:keywords: Python,Dependency Injection,Flask,Blueprints,Example
:description: This example demonstrates a usage of the Flask Blueprints and Dependency Injector.
This example shows how to use ``Dependency Injector`` with `Flask <https://flask.palletsprojects.com/en/1.1.x/>`_
blueprints.
The example application helps to search for repositories on the Github.
.. image:: images/flask.png
:width: 100%
:align: center
The source code is available on the `Github <https://github.com/ets-labs/python-dependency-injector/tree/master/examples/miniapps/flask-blueprints>`_.
Application structure
---------------------
Application has next structure:
.. code-block:: bash
./
├── githubnavigator/
│ ├── blueprints
│ │ ├── __init__.py
│ │ └── example.py
│ ├── templates
│ │ ├── base.html
│ │ └── index.py
│ ├── __init__.py
│ ├── application.py
│ ├── containers.py
│ ├── services.py
│ └── tests.py
├── config.yml
└── requirements.txt
Container
---------
Declarative container is defined in ``githubnavigator/containers.py``:
.. literalinclude:: ../../examples/miniapps/flask-blueprints/githubnavigator/containers.py
:language: python
Blueprints
----------
Blueprint's view has dependencies on search service and some config options. The dependencies are injected
using :ref:`wiring` feature.
Listing of ``githubnavigator/blueprints/example.py``:
.. literalinclude:: ../../examples/miniapps/flask-blueprints/githubnavigator/blueprints/example.py
:language: python
Application factory
-------------------
Application factory creates container, wires it with the blueprints, creates
``Flask`` app, and setup routes.
Listing of ``githubnavigator/application.py``:
.. literalinclude:: ../../examples/miniapps/flask-blueprints/githubnavigator/application.py
:language: python
Tests
-----
Tests use :ref:`provider-overriding` feature to replace github client with a mock ``githubnavigator/tests.py``:
.. literalinclude:: ../../examples/miniapps/flask-blueprints/githubnavigator/tests.py
:language: python
:emphasize-lines: 44,67
Sources
-------
Explore the sources on the `Github <https://github.com/ets-labs/python-dependency-injector/tree/master/examples/miniapps/flask-blueprints>`_.
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.. _flask-example:
Flask example
=============
.. meta::
:keywords: Python,Dependency Injection,Flask,Example
:description: This example demonstrates a usage of the Flask and Dependency Injector.
This example shows how to use ``Dependency Injector`` with `Flask <https://flask.palletsprojects.com/en/1.1.x/>`_.
The example application helps to search for repositories on the Github.
.. image:: images/flask.png
:width: 100%
:align: center
The source code is available on the `Github <https://github.com/ets-labs/python-dependency-injector/tree/master/examples/miniapps/flask>`_.
:ref:`flask-tutorial` demonstrates how to build this application step-by-step.
Application structure
---------------------
Application has next structure:
.. code-block:: bash
./
├── githubnavigator/
│ ├── templates
│ │ ├── base.html
│ │ └── index.py
│ ├── __init__.py
│ ├── application.py
│ ├── containers.py
│ ├── services.py
│ ├── tests.py
│ └── views.py
├── config.yml
└── requirements.txt
Container
---------
Declarative container is defined in ``githubnavigator/containers.py``:
.. literalinclude:: ../../examples/miniapps/flask/githubnavigator/containers.py
:language: python
Views
-----
View has dependencies on search service and some config options. The dependencies are injected
using :ref:`wiring` feature.
Listing of ``githubnavigator/views.py``:
.. literalinclude:: ../../examples/miniapps/flask/githubnavigator/views.py
:language: python
Application factory
-------------------
Application factory creates container, wires it with the ``views`` module, creates
``Flask`` app and setup routes.
Listing of ``githubnavigator/application.py``:
.. literalinclude:: ../../examples/miniapps/flask/githubnavigator/application.py
:language: python
Tests
-----
Tests use :ref:`provider-overriding` feature to replace github client with a mock ``githubnavigator/tests.py``:
.. literalinclude:: ../../examples/miniapps/flask/githubnavigator/tests.py
:language: python
:emphasize-lines: 44,67
Sources
-------
Explore the sources on the `Github <https://github.com/ets-labs/python-dependency-injector/tree/master/examples/miniapps/flask>`_.
.. include:: ../sponsor.rst
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========
.. meta::
:keywords: Python,DI,Dependency injection,IoC,Inversion of Control,Example
:description: Python dependency injection examples.
:keywords: Python,DI,Dependency injection,IoC,Inversion of Control
:description: Current section of documentation is designed to provide
several example mini applications that are built on the top
of inversion of control principle and powered by
"Dependency Injector" framework.
Explore the examples to see the ``Dependency Injector`` in action.
Current section of documentation is designed to provide several example mini
applications that are built according to the inversion of control principle
and powered by *Dependency Injector* framework.
.. toctree::
:maxdepth: 2
application-single-container
application-multiple-containers
decoupled-packages
boto3
django
flask
flask-blueprints
aiohttp
sanic
fastapi
fastapi-redis
fastapi-sqlalchemy
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Movie lister naive example
--------------------------
.. meta::
:description: Movie lister - is a naive example of dependency injection and
inversion of control containers on Python. Original example
was taken from Martin Fowler's article about dependency
injection and inversion of control.
This naive example was taken from Martin Fowler's article about dependency
injection and inversion of control: http://www.martinfowler.com/articles/injection.html
Like Martin says:
.. pull-quote::
*Like all of my examples it's one of those super-simple examples;
small enough to be unreal, but hopefully enough for you to visualize
what's going on without falling into the bog of a real example.*
While original Martin's MovieLister example was a bit modified here, it
makes sense to provide some description. So, the idea of this example is to
create ``movies`` library that can be configured to work with different
movie databases (csv, sqlite, etc...) and provide 2 main features:
1. List all movies that were directed by certain person.
2. List all movies that were released in certain year.
Also this example contains 3 mini applications that are based on ``movies``
library:
1. ``app_csv.py`` - list movies by certain criteria from csv file database.
2. ``app_db.py`` - list movies by certain criteria from sqlite database.
3. ``app_db_csv.py`` - list movies by certain criteria from csv file and
sqlite databases.
Instructions for running:
.. code-block:: bash
python app_csv.py
python app_db.py
python app_db_csv.py
Full code of example could be found on GitHub_.
Movies library
~~~~~~~~~~~~~~
Classes diagram:
.. image:: /images/miniapps/movie_lister/classes.png
:width: 100%
:align: center
Movies library structure:
.. code-block:: bash
/movies
/__init__.py
/finders.py
/listers.py
/models.py
Listing of ``movies/__init__.py``:
.. literalinclude:: ../../examples/miniapps/movie_lister/movies/__init__.py
:language: python
:linenos:
Example application
~~~~~~~~~~~~~~~~~~~
Example application structure:
.. code-block:: bash
/example
/__init__.py
/db.py
/main.py
Listing of ``examples/main.py``:
.. literalinclude:: ../../examples/miniapps/movie_lister/example/main.py
:language: python
:linenos:
Listing of ``examples/db.py``:
.. literalinclude:: ../../examples/miniapps/movie_lister/example/db.py
:language: python
:linenos:
Csv application
~~~~~~~~~~~~~~~
Listing of ``app_csv.py``:
.. literalinclude:: ../../examples/miniapps/movie_lister/app_csv.py
:language: python
:linenos:
Database application
~~~~~~~~~~~~~~~~~~~~
Listing of ``app_db.py``:
.. literalinclude:: ../../examples/miniapps/movie_lister/app_db.py
:language: python
:linenos:
Csv and database application
~~~~~~~~~~~~~~~~~~~~~~~~~~~~
Listing of ``app_db_csv.py``:
.. literalinclude:: ../../examples/miniapps/movie_lister/app_db_csv.py
:language: python
:linenos:
.. _GitHub: https://github.com/ets-labs/python-dependency-injector/tree/master/examples/miniapps/movie_lister

View File

@ -1,82 +0,0 @@
.. _sanic-example:
Sanic example
==============
.. meta::
:keywords: Python,Dependency Injection,Sanic,Example
:description: This example demonstrates a usage of the Sanic and Dependency Injector.
This example shows how to use ``Dependency Injector`` with `Sanic <https://sanic.readthedocs.io/en/latest/>`_.
The example application is a REST API that searches for funny GIFs on the `Giphy <https://giphy.com/>`_.
The source code is available on the `Github <https://github.com/ets-labs/python-dependency-injector/tree/master/examples/miniapps/sanic>`_.
Application structure
---------------------
Application has next structure:
.. code-block:: bash
./
├── giphynavigator/
│ ├── __init__.py
│ ├── __main__.py
│ ├── application.py
│ ├── containers.py
│ ├── giphy.py
│ ├── handlers.py
│ ├── services.py
│ └── tests.py
├── config.yml
└── requirements.txt
Container
---------
Declarative container is defined in ``giphynavigator/containers.py``:
.. literalinclude:: ../../examples/miniapps/sanic/giphynavigator/containers.py
:language: python
Handlers
--------
Handler has dependencies on search service and some config options. The dependencies are injected
using :ref:`wiring` feature.
Listing of ``giphynavigator/handlers.py``:
.. literalinclude:: ../../examples/miniapps/sanic/giphynavigator/handlers.py
:language: python
Application factory
-------------------
Application factory creates container, wires it with the ``handlers`` module, creates
``Sanic`` app and setup routes.
Listing of ``giphynavigator/application.py``:
.. literalinclude:: ../../examples/miniapps/sanic/giphynavigator/application.py
:language: python
Tests
-----
Tests use :ref:`provider-overriding` feature to replace giphy client with a mock ``giphynavigator/tests.py``:
.. literalinclude:: ../../examples/miniapps/sanic/giphynavigator/tests.py
:language: python
:emphasize-lines: 34,61,75
Sources
-------
Explore the sources on the `Github <https://github.com/ets-labs/python-dependency-injector/tree/master/examples/miniapps/sanic>`_.
.. include:: ../sponsor.rst
.. disqus::

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=================================================================
Dependency Injector --- Dependency injection framework for Python
=================================================================
======================================================================
Dependency Injector --- Dependency injection microframework for Python
======================================================================
.. meta::
:google-site-verification: V1hlKfpgL3AARAElwFcqP4qW1Smsx5bKSRU8O86i20Y
:keywords: Python,Dependency injection,DI,Inversion of Control,IoC,
IoC Container,Factory, Singleton, Design Patterns
:description: Dependency Injector is a dependency injection framework
for Python. It helps to maintain you application structure.
It was designed to be unified, developer-friendly
tool that helps to implement dependency injection design
pattern in formal, pretty, Pythonic way. Dependency Injector
provides implementations of such popular design patterns
like IoC container, Factory and Singleton. Dependency
Injector providers are implemented as C extension types
using Cython.
:keywords: Python,DI,Dependency injection,IoC,Inversion of Control
:google-site-verification: 6it89zX0_wccKEhAqbAiYQooS95f0BA8YfesHk6bsNA
:description: Dependency Injector is a dependency injection microframework
for Python. It was designed to be unified, developer-friendly
tool that helps to implement dependency injection pattern in
formal, pretty, Pythonic way.
.. _index:
*Dependency Injector* is a dependency injection microframework for Python.
It was designed to be unified, developer-friendly tool that helps to implement
dependency injection pattern in formal, pretty, Pythonic way.
.. image:: https://img.shields.io/pypi/v/dependency_injector.svg
:target: https://pypi.org/project/dependency-injector/
:alt: Latest Version
*Dependency Injector* framework key features are:
.. image:: https://img.shields.io/pypi/l/dependency_injector.svg
:target: https://pypi.org/project/dependency-injector/
:alt: License
+ Easy, smart, pythonic style.
+ Obvious, clear structure.
+ Extensibility and flexibility.
+ Memory efficiency.
+ Thread safety.
+ Documentation.
+ Semantic versioning.
.. image:: https://img.shields.io/pypi/pyversions/dependency_injector.svg
:target: https://pypi.org/project/dependency-injector/
:alt: Supported Python versions
Status
------
.. image:: https://img.shields.io/pypi/implementation/dependency_injector.svg
:target: https://pypi.org/project/dependency-injector/
:alt: Supported Python implementations
.. image:: https://static.pepy.tech/badge/dependency-injector
:target: https://pepy.tech/project/dependency-injector
:alt: Downloads
.. image:: https://static.pepy.tech/badge/dependency-injector/month
:target: https://pepy.tech/project/dependency-injector
:alt: Downloads
.. image:: https://static.pepy.tech/badge/dependency-injector/week
:target: https://pepy.tech/project/dependency-injector
:alt: Downloads
.. image:: https://img.shields.io/pypi/wheel/dependency-injector.svg
:target: https://pypi.org/project/dependency-injector/
:alt: Wheel
.. image:: https://img.shields.io/github/actions/workflow/status/ets-labs/python-dependency-injector/tests-and-linters.yml?branch=master
:target: https://github.com/ets-labs/python-dependency-injector/actions
:alt: Build Status
.. image:: https://coveralls.io/repos/github/ets-labs/python-dependency-injector/badge.svg?branch=master
:target: https://coveralls.io/github/ets-labs/python-dependency-injector?branch=master
:alt: Coverage Status
``Dependency Injector`` is a dependency injection framework for Python.
It helps implementing the dependency injection principle.
Key features of the ``Dependency Injector``:
- **Providers**. Provides ``Factory``, ``Singleton``, ``Callable``, ``Coroutine``, ``Object``,
``List``, ``Dict``, ``Configuration``, ``Resource``, ``Dependency``, and ``Selector`` providers
that help assemble your objects. See :ref:`providers`.
- **Overriding**. Can override any provider by another provider on the fly. This helps in testing
and configuring dev/stage environment to replace API clients with stubs etc. See
:ref:`provider-overriding`.
- **Configuration**. Reads configuration from ``yaml``, ``ini``, and ``json`` files, ``pydantic`` settings,
environment variables, and dictionaries. See :ref:`configuration-provider`.
- **Resources**. Helps with initialization and configuring of logging, event loop, thread
or process pool, etc. Can be used for per-function execution scope in tandem with wiring.
See :ref:`resource-provider`.
- **Containers**. Provides declarative and dynamic containers. See :ref:`containers`.
- **Wiring**. Injects dependencies into functions and methods. Helps integrate with
other frameworks: Django, Flask, Aiohttp, Sanic, FastAPI, etc. See :ref:`wiring`.
- **Asynchronous**. Supports asynchronous injections. See :ref:`async-injections`.
- **Typing**. Provides typing stubs, ``mypy``-friendly. See :ref:`provider-typing`.
- **Performance**. Fast. Written in ``Cython``.
- **Maturity**. Mature and production-ready. Well-tested, documented, and supported.
.. code-block:: python
from dependency_injector import containers, providers
from dependency_injector.wiring import Provide, inject
class Container(containers.DeclarativeContainer):
config = providers.Configuration()
api_client = providers.Singleton(
ApiClient,
api_key=config.api_key,
timeout=config.timeout,
)
service = providers.Factory(
Service,
api_client=api_client,
)
@inject
def main(service: Service = Provide[Container.service]) -> None:
...
if __name__ == "__main__":
container = Container()
container.config.api_key.from_env("API_KEY", required=True)
container.config.timeout.from_env("TIMEOUT", as_=int, default=5)
container.wire(modules=[__name__])
main() # <-- dependency is injected automatically
with container.api_client.override(mock.Mock()):
main() # <-- overridden dependency is injected automatically
With the ``Dependency Injector``, object assembling is consolidated in the container.
Dependency injections are defined explicitly.
This makes it easier to understand and change how the application works.
.. figure:: https://raw.githubusercontent.com/wiki/ets-labs/python-dependency-injector/img/di-readme.svg
:target: https://github.com/ets-labs/python-dependency-injector
Explore the documentation to know more about the ``Dependency Injector``.
.. _contents:
+---------------------------------------+----------------------------------------------------------------------------------------+
| *PyPi* | .. image:: https://img.shields.io/pypi/v/dependency_injector.svg |
| | :target: https://pypi.python.org/pypi/dependency_injector/ |
| | :alt: Latest Version |
| | .. image:: https://img.shields.io/pypi/l/dependency_injector.svg |
| | :target: https://pypi.python.org/pypi/dependency_injector/ |
| | :alt: License |
+---------------------------------------+----------------------------------------------------------------------------------------+
| *Python versions and implementations* | .. image:: https://img.shields.io/pypi/pyversions/dependency_injector.svg |
| | :target: https://pypi.python.org/pypi/dependency_injector/ |
| | :alt: Supported Python versions |
| | .. image:: https://img.shields.io/pypi/implementation/dependency_injector.svg |
| | :target: https://pypi.python.org/pypi/dependency_injector/ |
| | :alt: Supported Python implementations |
+---------------------------------------+----------------------------------------------------------------------------------------+
| *Builds and tests coverage* | .. image:: https://travis-ci.org/ets-labs/python-dependency-injector.svg?branch=master |
| | :target: https://travis-ci.org/ets-labs/python-dependency-injector |
| | :alt: Build Status |
| | .. image:: https://coveralls.io/repos/ets-labs/python-dependency-injector/badge.svg |
| | :target: https://coveralls.io/r/ets-labs/python-dependency-injector |
| | :alt: Coverage Status |
+---------------------------------------+----------------------------------------------------------------------------------------+
Contents
--------
.. toctree::
:maxdepth: 2
.. toctree::
:maxdepth: 2
introduction/index
examples/index
tutorials/index
providers/index
containers/index
wiring
examples-other/index
api/index
main/feedback
main/changelog
introduction/index
main/installation
providers/index
containers/index
examples/index
api/index
main/feedback
main/changelog

View File

@ -1,315 +1,127 @@
Dependency injection and inversion of control in Python
=======================================================
-------------------------------------------------------
.. meta::
:keywords: Python,DI,Dependency injection,IoC,Inversion of Control,Example
:description: This page describes a usage of the dependency injection and inversion of control
in Python. It contains Python examples that show how to implement dependency
injection. It demonstrates a usage of the dependency injection framework
Dependency Injector, its container, Factory, Singleton and Configuration
providers. The example show how to use Dependency Injector providers overriding
feature for testing or configuring project in different environments and explains
why it's better than monkey-patching.
:keywords: Python,DI,Dependency injection,IoC,Inversion of Control
:description: This article describes benefits of dependency injection and
inversion of control for Python applications. Also it
contains some Python examples that show how dependency
injection and inversion could be implemented.
History
~~~~~~~
Originally, dependency injection pattern got popular in languages with static
typing, like Java. Dependency injection framework can
significantly improve flexibility of the language with static typing. Also,
implementation of dependency injection framework for language with static
typing is not something that one can do shortly, it could be quite complex
thing to be done well.
While Python is very flexible interpreted language with dynamic typing, there
is a meaning that dependency injection doesn't work for it as well, as it does
for Java. Also there is a meaning that dependency injection framework is
something that Python developer would not ever need, cause dependency injection
could be implemented easily using language fundamentals.
Discussion
~~~~~~~~~~
It is true.
Partly.
Dependency injection, as a software design pattern, has number of
advantages that are common for each language (including Python):
+ Dependency Injection decreases coupling between a class and its dependency.
+ Because dependency injection doesn't require any change in code behavior it
can be applied to legacy code as a refactoring. The result is clients that
are more independent and that are easier to unit test in isolation using
stubs or mock objects that simulate other objects not under test. This ease
of testing is often the first benefit noticed when using dependency
injection.
+ Dependency injection can be used to externalize a system's configuration
details into configuration files allowing the system to be reconfigured
without recompilation (rebuilding). Separate configurations can be written
for different situations that require different implementations of
components. This includes, but is not limited to, testing.
+ Reduction of boilerplate code in the application objects since all work to
initialize or set up dependencies is handled by a provider component.
+ Dependency injection allows a client to remove all knowledge of a concrete
implementation that it needs to use. This helps isolate the client from the
impact of design changes and defects. It promotes reusability, testability
and maintainability.
+ Dependency injection allows a client the flexibility of being configurable.
Only the client's behavior is fixed. The client may act on anything that
supports the intrinsic interface the client expects.
.. note::
While improved testability is one the first benefits of using dependency
injection, it could be easily overwhelmed by monkey-patching technique,
that works absolutely great in Python (you can monkey-patch anything,
anytime). At the same time, monkey-patching has nothing similar with
other advantages defined above. Also monkey-patching technique is
something that could be considered like too dirty to be used in production.
The complexity of dependency injection pattern implementation in Python is
definitely quite lower than in other languages (even with dynamic typing).
.. note::
Low complexity of dependency injection pattern implementation in Python
still means that some code should be written, reviewed, tested and
supported.
Talking about inversion of control, it is a software design principle that
also works for each programming language, not dependending on its typing type.
Inversion of control is used to increase modularity of the program and make
it extensible.
Main design purposes of using inversion of control are:
+ To decouple the execution of a task from implementation.
+ To focus a module on the task it is designed for.
+ To free modules from assumptions about how other systems do what they do and
instead rely on contracts.
+ To prevent side effects when replacing a module.
Example
~~~~~~~
Let's go through next example:
.. literalinclude:: ../../examples/ioc_di_demos/car_engine.py
:language: python
:linenos:
``Car`` **creates** an ``Engine`` during its creation. Really? Does it make
more sense than creating an ``Engine`` separately and then
**inject (put) it into** ``Car`` when ``Car`` is being created?
.. literalinclude:: ../../examples/ioc_di_demos/car_engine_ioc.py
:language: python
:linenos:
Previous example may look more obvious and gives a chance to start getting
other benefits of dependency injection and inversion of control, but creation
of ``Car`` instances became a bit harder cause now ``Engine`` injections
should be done manually every time when ``Car`` instances are being created.
Let's automate ``Engine`` into ``Car`` injections using *Dependency Injector*:
.. literalinclude:: ../../examples/ioc_di_demos/car_engine_ioc_container.py
:language: python
:linenos:
Originally dependency injection pattern got popular in languages with static typing like Java.
Dependency injection is a principle that helps to achieve an inversion of control. A
dependency injection framework can significantly improve the flexibility of a language
with static typing. Implementation of a dependency injection framework for a language
with static typing is not something that one can do quickly. It will be a quite complex thing
to be done well. And will take time.
.. note::
Python is an interpreted language with dynamic typing. There is an opinion that dependency
injection doesn't work for it as well as it does for Java. A lot of the flexibility is already
built-in. Also, there is an opinion that a dependency injection framework is something that
Python developer rarely needs. Python developers say that dependency injection can be implemented
easily using language fundamentals.
``Container`` from previous example is an inversion of control container.
It contains a collection of component providers that could be injected
into each other.
This page describes the advantages of applying dependency injection in Python. It
contains Python examples that show how to implement dependency injection. It demonstrates the usage
of the ``Dependency Injector`` framework, its container, ``Factory``, ``Singleton``,
and ``Configuration`` providers. The example shows how to use providers' overriding feature
of ``Dependency Injector`` for testing or re-configuring a project in different environments and
explains why it's better than monkey-patching.
What is dependency injection?
-----------------------------
Let's see what the dependency injection is.
Dependency injection is a principle that helps to decrease coupling and increase cohesion.
.. image:: images/coupling-cohesion.png
What is coupling and cohesion?
Coupling and cohesion are about how tough the components are tied.
- **High coupling**. If the coupling is high it's like using superglue or welding. No easy way
to disassemble.
- **High cohesion**. High cohesion is like using screws. Quite easy to disassemble and
re-assemble in a different way. It is an opposite to high coupling.
Cohesion often correlates with coupling. Higher cohesion usually leads to lower coupling and vice versa.
Low coupling brings flexibility. Your code becomes easier to change and test.
How to implement the dependency injection?
Objects do not create each other anymore. They provide a way to inject the dependencies instead.
Before:
.. code-block:: python
import os
class ApiClient:
def __init__(self) -> None:
self.api_key = os.getenv("API_KEY") # <-- dependency
self.timeout = int(os.getenv("TIMEOUT")) # <-- dependency
class Service:
def __init__(self) -> None:
self.api_client = ApiClient() # <-- dependency
def main() -> None:
service = Service() # <-- dependency
...
if __name__ == "__main__":
main()
After:
.. code-block:: python
import os
class ApiClient:
def __init__(self, api_key: str, timeout: int) -> None:
self.api_key = api_key # <-- dependency is injected
self.timeout = timeout # <-- dependency is injected
class Service:
def __init__(self, api_client: ApiClient) -> None:
self.api_client = api_client # <-- dependency is injected
def main(service: Service) -> None: # <-- dependency is injected
...
if __name__ == "__main__":
main(
service=Service(
api_client=ApiClient(
api_key=os.getenv("API_KEY"),
timeout=int(os.getenv("TIMEOUT")),
),
),
)
``ApiClient`` is decoupled from knowing where the options come from. You can read a key and a
timeout from a configuration file or even get them from a database.
``Service`` is decoupled from the ``ApiClient``. It does not create it anymore. You can provide a
stub or other compatible object.
Function ``main()`` is decoupled from ``Service``. It receives it as an argument.
Flexibility comes with a price.
Now you need to assemble and inject the objects like this:
.. code-block:: python
main(
service=Service(
api_client=ApiClient(
api_key=os.getenv("API_KEY"),
timeout=int(os.getenv("TIMEOUT")),
),
),
)
The assembly code might get duplicated and it'll become harder to change the application structure.
Here comes the ``Dependency Injector``.
What does the Dependency Injector do?
-------------------------------------
With the dependency injection pattern, objects lose the responsibility of assembling
the dependencies. The ``Dependency Injector`` absorbs that responsibility.
``Dependency Injector`` helps to assemble and inject the dependencies.
It provides a container and providers that help you with the objects assembly.
When you need an object you place a ``Provide`` marker as a default value of a
function argument. When you call this function, framework assembles and injects
the dependency.
.. code-block:: python
from dependency_injector import containers, providers
from dependency_injector.wiring import Provide, inject
class Container(containers.DeclarativeContainer):
config = providers.Configuration()
api_client = providers.Singleton(
ApiClient,
api_key=config.api_key,
timeout=config.timeout,
)
service = providers.Factory(
Service,
api_client=api_client,
)
@inject
def main(service: Service = Provide[Container.service]) -> None:
...
if __name__ == "__main__":
container = Container()
container.config.api_key.from_env("API_KEY", required=True)
container.config.timeout.from_env("TIMEOUT", as_=int, default=5)
container.wire(modules=[__name__])
main() # <-- dependency is injected automatically
with container.api_client.override(mock.Mock()):
main() # <-- overridden dependency is injected automatically
When you call the ``main()`` function the ``Service`` dependency is assembled and injected automatically.
When you do testing, you call the ``container.api_client.override()`` method to replace the real API
client with a mock. When you call ``main()``, the mock is injected.
You can override any provider with another provider.
It also helps you in a re-configuring project for different environments: replace an API client
with a stub on the dev or stage.
Objects assembling is consolidated in a container. Dependency injections are defined explicitly.
This makes it easier to understand and change how an application works.
Testing, Monkey-patching and dependency injection
-------------------------------------------------
The testability benefit is opposed to monkey-patching.
In Python, you can monkey-patch anything, anytime. The problem with monkey-patching is
that it's too fragile. The cause of it is that when you monkey-patch you do something that
wasn't intended to be done. You monkey-patch the implementation details. When implementation
changes the monkey-patching is broken.
With dependency injection, you patch the interface, not an implementation. This is a way more
stable approach.
Also, monkey-patching is way too dirty to be used outside of the testing code for
re-configuring the project for the different environments.
Conclusion
----------
Dependency injection provides you with three advantages:
- **Flexibility**. The components are loosely coupled. You can easily extend or change the
functionality of a system by combining the components in a different way. You even can do it on
the fly.
- **Testability**. Testing is easier because you can easily inject mocks instead of real objects
that use API or database, etc.
- **Clearness and maintainability**. Dependency injection helps you reveal the dependencies.
Implicit becomes explicit. And "Explicit is better than implicit" (PEP 20 - The Zen of Python).
You have all the components and dependencies defined explicitly in a container. This
provides an overview and control of the application structure. It is easier to understand and
change it.
Is it worth applying dependency injection in Python?
It depends on what you build. The advantages above are not too important if you use Python as a
scripting language. The picture is different when you use Python to create an application. The
larger the application the more significant the benefits.
Is it worth using a framework for applying dependency injection?
The complexity of the dependency injection pattern implementation in Python is
lower than in other languages but it's still in place. It doesn't mean you have to use a
framework but using a framework is beneficial because the framework is:
- Already implemented
- Tested on all platforms and versions of Python
- Documented
- Supported
- Other engineers are familiar with it
An advice at last:
- **Give it a try**. Dependency injection is counter-intuitive. Our nature is that
when we need something the first thought that comes to our mind is to go and get it. Dependency
injection is just like "Wait, I need to state a need instead of getting something right away".
It's like a little investment that will pay-off later. The advice is to just give it a try for
two weeks. This time will be enough for getting your own impression. If you don't like it you
won't lose too much.
- **Common sense first**. Use common sense when applying dependency injection. It is a good
principle, but not a silver bullet. If you do it too much you will reveal too many of the
implementation details. Experience comes with practice and time.
What's next?
------------
Choose one of the following as a next step:
- Look at the application examples:
- :ref:`application-single-container`
- :ref:`application-multiple-containers`
- :ref:`decoupled-packages`
- :ref:`boto3-example`
- :ref:`django-example`
- :ref:`flask-example`
- :ref:`flask-blueprints-example`
- :ref:`aiohttp-example`
- :ref:`sanic-example`
- :ref:`fastapi-example`
- :ref:`fastapi-redis-example`
- :ref:`fastapi-sqlalchemy-example`
- Pass the tutorials:
- :ref:`flask-tutorial`
- :ref:`aiohttp-tutorial`
- :ref:`asyncio-daemon-tutorial`
- :ref:`cli-tutorial`
- Know more about the ``Dependency Injector`` :ref:`key-features`
- Know more about the :ref:`providers`
- Know more about the :ref:`wiring`
- Go to the :ref:`contents`
Useful links
------------
A few useful links related to a dependency injection design pattern for further reading:
+ https://en.wikipedia.org/wiki/Dependency_injection
+ https://martinfowler.com/articles/injection.html
+ https://github.com/ets-labs/python-dependency-injector
+ https://pypi.org/project/dependency-injector/
.. include:: ../sponsor.rst
.. disqus::
Assuming this, ``Container`` could be one and the only place, where
application's structure is being managed on the high level.

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.. meta::
:keywords: Python,DI,Dependency injection,IoC,Inversion of Control
:description: Current section of the documentation is provides an
overview of the dependency injection, inversion of
control and Dependency Injector framework.
:description: Current section of documentation is designed to give some
overview about dependency injection pattern, inversion of
control principle and "Dependency Injector" framework.
The current section of the documentation provides an overview of the
dependency injection, inversion of control, and the ``Dependency Injector`` framework.
Current section of documentation is designed to give some overview about
dependency injection pattern, inversion of control principle and
*Dependency Injector* framework.
.. toctree::
:maxdepth: 2
what_is_di
di_in_python
key_features
installation
structure

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Installation
============
``Dependency Injector`` is available on `PyPI <https://pypi.org/project/dependency-injector/>`_.
To install the latest version you can use ``pip``:
.. code-block:: bash
pip install dependency-injector
Some modules of the ``Dependency Injector`` are implemented as C extensions.
``Dependency Injector`` is distributed as a pre-compiled wheels. Wheels are
available for all supported Python versions on Linux, Windows, and MacOS.
Linux distribution uses `manylinux <https://github.com/pypa/manylinux>`_.
If there is no appropriate wheel for your environment (Python version and OS)
installer will compile the package from sources on your machine. You'll need
a C compiler and Python header files.
To verify the installed version:
.. code-block:: bash
>>> import dependency_injector
>>> dependency_injector.__version__
'4.39.0'
.. note::
When adding ``Dependency Injector`` to ``pyproject.toml`` or ``requirements.txt``
don't forget to pin the version to the current major:
.. code-block:: bash
dependency-injector>=4.0,<5.0
*The next major version can be incompatible.*
All releases are available on the `PyPI release history page <https://pypi.org/project/dependency-injector/#history>`_.
Each release has an appropriate tag. The tags are available on the
`GitHub releases page <https://github.com/ets-labs/python-dependency-injector/releases>`_.
.. disqus::

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.. _key-features:
Key features
------------
Key features of Dependency Injector
-----------------------------------
.. meta::
:keywords: Python,DI,Dependency injection,IoC,Inversion of Control
:description: This article describes key features of the Dependency Injector
framework.
:description: This article describes key features of "Dependency Injector"
framework. It also provides some cases and recommendations
about usage of "Dependency Injector" framework.
Key features of the ``Dependency Injector``:
*Dependency Injector* is a dependency injection framework for Python projects.
It was designed to be unified, developer-friendly tool for managing any kind
of Python objects and their dependencies in formal, pretty way.
- **Providers**. Provides ``Factory``, ``Singleton``, ``Callable``, ``Coroutine``, ``Object``,
``List``, ``Dict``, ``Configuration``, ``Resource``, ``Dependency``, and ``Selector`` providers
that help assemble your objects. See :ref:`providers`.
- **Overriding**. Can override any provider by another provider on the fly. This helps in testing
and configuring dev/stage environment to replace API clients with stubs etc. See
:ref:`provider-overriding`.
- **Configuration**. Reads configuration from ``yaml``, ``ini``, and ``json`` files, ``pydantic`` settings,
environment variables, and dictionaries. See :ref:`configuration-provider`.
- **Resources**. Helps with initialization and configuring of logging, event loop, thread
or process pool, etc. Can be used for per-function execution scope in tandem with wiring.
See :ref:`resource-provider`.
- **Containers**. Provides declarative and dynamic containers. See :ref:`containers`.
- **Wiring**. Injects dependencies into functions and methods. Helps integrate with
other frameworks: Django, Flask, Aiohttp, Sanic, FastAPI, etc. See :ref:`wiring`.
- **Asynchronous**. Supports asynchronous injections. See :ref:`async-injections`.
- **Typing**. Provides typing stubs, ``mypy``-friendly. See :ref:`provider-typing`.
- **Performance**. Fast. Written in ``Cython``.
- **Maturity**. Mature and production-ready. Well-tested, documented, and supported.
*Dependency Injector* framework key features are:
The framework stands on the `PEP20 (The Zen of Python) <https://www.python.org/dev/peps/pep-0020/>`_ principle:
+ Easy, smart, pythonic style.
+ Obvious, clear structure.
+ Extensibility and flexibility.
+ Memory efficiency.
+ Thread safety.
+ Documentation.
+ Semantic versioning.
.. code-block:: plain
*Dependency Injector* framework could be used in different application types:
Explicit is better than implicit
+ Web applications based on Flask, Django or any other web framework.
+ Asynchronous applications based on asyncio, Tornado and Twisted.
+ Standalone frameworks and libraries.
+ GUI applications.
You need to specify how to assemble and where to inject the dependencies explicitly.
*Dependency Injector* framework could be integrated on different project
stages:
The power of the framework is in its simplicity.
``Dependency Injector`` is a simple tool for the powerful concept.
+ It could be used in the beginning of development of new applications.
+ It could be integrated into applications that are in active development
stage.
+ It could be used for refactoring of legacy applications.
.. disqus::
Components of *Dependency Injector* framework could be used:
+ In composition with each other.
+ Separately between each other.
Main idea of *Dependency Injector* framework is to be useful tool for the
right thing.

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Structure of Dependency Injector
--------------------------------
.. meta::
:keywords: Python,DI,Dependency injection,IoC,Inversion of Control
:description: This article describes "Dependency Injector" framework
components and their interaction between each other.
Providers and containers are the former components of
the framework.
Current section describes *Dependency Injector* main entities and their
interaction between each other.
.. image:: /images/internals.png
:width: 100%
:align: center
There are 2 main entities: providers & containers.
Providers
~~~~~~~~~
Providers are strategies of accessing objects. For example,
:py:class:`dependency_injector.providers.Factory` creates new instance
of provided class every time it is called.
:py:class:`dependency_injector.providers.Singleton` creates provided
instance once and returns it on every next call. Base class is -
:py:class:`dependency_injector.providers.Provider`.
Providers could be:
+ Injected into each other.
+ Overridden by each other.
+ Extended.
Containers
~~~~~~~~~~
Containers are collections of providers. They are used for grouping
of providers by some principles. Base class is -
:py:class:`dependency_injector.containers.DeclarativeContainer`.
Containers could be:
+ Overridden by each other.
+ Copied from each other.
+ Extended.

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What is dependency injection and inversion of control?
------------------------------------------------------
.. meta::
:keywords: Python,DI,Dependency injection,IoC,Inversion of Control
:description: This article provides definition of dependency injection,
inversion of control and dependency inversion. It contains
example code in Python that is refactored to be following
inversion of control principle.
Definition
~~~~~~~~~~
Wikipedia provides quite good definitions of dependency injection pattern
and related principles:
.. glossary::
`Dependency injection`_
In software engineering, dependency injection is a software design
pattern that implements inversion of control for resolving
dependencies. A dependency is an object that can be used (a service).
An injection is the passing of a dependency to a dependent object (a
client) that would use it. The service is made part of the client's
state. Passing the service to the client, rather than allowing a
client to build or find the service, is the fundamental requirement of
the pattern.
Dependency injection allows a program design to follow the dependency
inversion principle. The client delegates to external code (the
injector) the responsibility of providing its dependencies. The client
is not allowed to call the injector code. It is the injecting code
that constructs the services and calls the client to inject them. This
means the client code does not need to know about the injecting code.
The client does not need to know how to construct the services. The
client does not need to know which actual services it is using. The
client only needs to know about the intrinsic interfaces of the
services because these define how the client may use the services.
This separates the responsibilities of use and construction.
`Inversion of control`_
In software engineering, inversion of control (IoC) describes a design
in which custom-written portions of a computer program receive the
flow of control from a generic, reusable library. A software
architecture with this design inverts control as compared to
traditional procedural programming: in traditional programming, the
custom code that expresses the purpose of the program calls into
reusable libraries to take care of generic tasks, but with inversion
of control, it is the reusable code that calls into the custom, or
task-specific, code.
Inversion of control is used to increase modularity of the program and
make it extensible, and has applications in object-oriented
programming and other programming paradigms. The term was popularized
by Robert C. Martin and Martin Fowler.
The term is related to, but different from, the dependency inversion
principle, which concerns itself with decoupling dependencies between
high-level and low-level layers through shared abstractions.
`Dependency inversion`_
In object-oriented programming, the dependency inversion principle
refers to a specific form of decoupling software modules. When
following this principle, the conventional dependency relationships
established from high-level, policy-setting modules to low-level,
dependency modules are reversed, thus rendering high-level modules
independent of the low-level module implementation details. The
principle states:
+ High-level modules should not depend on low-level modules.
Both should depend on abstractions.
+ Abstractions should not depend on details.
Details should depend on abstractions.
The principle inverts the way some people may think about
object-oriented design, dictating that both high- and low-level
objects must depend on the same abstraction.
Example
~~~~~~~
Let's go through the code of ``example.py``:
.. literalinclude:: ../../examples/ioc_di_demos/example.py
:language: python
:linenos:
At some point, things defined above mean, that the code from ``example.py``,
could look different, like in ``example_ioc.py``:
.. literalinclude:: ../../examples/ioc_di_demos/example_ioc.py
:language: python
:linenos:
Best explanation, ever
~~~~~~~~~~~~~~~~~~~~~~
Some times ago `user198313`_ posted awesome `question`_ about dependency
injection on `StackOverflow`_:
.. note::
How to explain dependency injection to a 5-year-old?
And `John Munsch`_ provided absolutely Great answer:
.. note::
When you go and get things out of the refrigerator for yourself, you can
cause problems. You might leave the door open, you might get something
Mommy or Daddy doesn't want you to have. You might even be looking for
something we don't even have or which has expired.
What you should be doing is stating a need, "I need something to drink
with lunch," and then we will make sure you have something when you sit
down to eat.
.. _Dependency injection: http://en.wikipedia.org/wiki/Dependency_injection
.. _Inversion of control: https://en.wikipedia.org/wiki/Inversion_of_control
.. _Dependency inversion: https://en.wikipedia.org/wiki/Dependency_inversion_principle
.. _StackOverflow: http://stackoverflow.com/
.. _question: http://stackoverflow.com/questions/1638919/how-to-explain-dependency-injection-to-a-5-year-old/1639186
.. _user198313: http://stackoverflow.com/users/198313/user198313
.. _John Munsch: http://stackoverflow.com/users/31899/john-munsch

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Feedback
========
To post a question, bug report, a feature proposal or get some help open a
`Github Issue <https://github.com/ets-labs/python-dependency-injector/issues>`_ or leave a comment
below.
Feel free to post questions, bugs, feature requests, proposals etc. on
*Dependency Injector* GitHub Issues:
.. disqus::
https://github.com/ets-labs/python-dependency-injector/issues
Your feedback is quite important!

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Installation
============
*Dependency Injector* framework is distributed by PyPi_.
Latest stable version (and all previous versions) of *Dependency Injector*
framework can be installed from PyPi_:
.. code-block:: bash
# Installing latest version:
pip install dependency_injector
# Installing particular version:
pip install dependency_injector==2.0.0
Sources can be cloned from GitHub_:
.. code-block:: bash
git clone https://github.com/ets-labs/python-dependency-injector.git
Also all *Dependency Injector* releases can be downloaded from
`GitHub releases page`_.
Verification of currently installed version could be done using
:py:obj:`dependency_injector.VERSION` constant:
.. code-block:: bash
>>> from dependency_injector import VERSION
>>> VERSION
'2.0.0'
.. _PyPi: https://pypi.python.org/pypi/dependency_injector
.. _GitHub: https://github.com/ets-labs/python-dependency-injector
.. _GitHub releases page: https://github.com/ets-labs/python-dependency-injector/releases

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.. _aggregate-provider:
Aggregate provider
==================
.. meta::
:keywords: Python,DI,Dependency injection,IoC,Inversion of Control,Configuration,Injection,
Aggregate,Polymorphism,Environment Variable,Flexibility
:description: Aggregate provider aggregates other providers.
This page demonstrates how to implement the polymorphism and increase the
flexibility of your application using the Aggregate provider.
:py:class:`Aggregate` provider aggregates a group of other providers.
.. currentmodule:: dependency_injector.providers
.. literalinclude:: ../../examples/providers/aggregate.py
:language: python
:lines: 3-
:emphasize-lines: 24-27
Each provider in the ``Aggregate`` is associated with a key. You can call aggregated providers by providing
their key as a first argument. All positional and keyword arguments following the key will be forwarded to
the called provider:
.. code-block:: python
yaml_reader = container.config_readers("yaml", "./config.yml", foo=...)
You can also retrieve an aggregated provider by providing its key as an attribute name:
.. code-block:: python
yaml_reader = container.config_readers.yaml("./config.yml", foo=...)
To retrieve a dictionary of aggregated providers, use ``.providers`` attribute:
.. code-block:: python
container.config_readers.providers == {
"yaml": <YAML provider>,
"json": <JSON provider>,
}
.. note::
You can not override the ``Aggregate`` provider.
.. note::
When you inject the ``Aggregate`` provider, it is passed "as is".
To use non-string keys or string keys with ``.`` and ``-``, provide a dictionary as a positional argument:
.. code-block:: python
aggregate = providers.Aggregate({
SomeClass: providers.Factory(...),
"key.with.periods": providers.Factory(...),
"key-with-dashes": providers.Factory(...),
})
.. seealso::
:ref:`selector-provider` to make injections based on a configuration value, environment variable, or a result of a callable.
``Aggregate`` provider is different from the :ref:`selector-provider`. ``Aggregate`` provider doesn't select which provider
to inject and doesn't have a selector. It is a group of providers and is always injected "as is". The rest of the interface
of both providers is similar.
.. note::
``Aggregate`` provider is a successor of :ref:`factory-aggregate-provider` provider. ``Aggregate`` provider doesn't have
a restriction on the provider type, while ``FactoryAggregate`` aggregates only ``Factory`` providers.
.. disqus::

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.. _async-injections:
Asynchronous injections
=======================
.. meta::
:keywords: Python,DI,Dependency injection,IoC,Inversion of Control,Providers,Async,Injections,Asynchronous,Await,
Asyncio
:description: Dependency Injector providers support asynchronous injections. This page
demonstrates how make asynchronous dependency injections in Python.
Providers support asynchronous injections.
.. literalinclude:: ../../examples/providers/async.py
:language: python
:emphasize-lines: 26-29
:lines: 3-
If provider has any awaitable injections it switches into async mode. In async mode provider always returns awaitable.
This causes a cascade effect:
.. code-block:: bash
provider1() <── Async mode enabled <──┐
│ │
├──> provider2() │
│ │
├──> provider3() <── Async mode enabled <──┤
│ │ │
│ └──> provider4() <── Async provider ───────┘
└──> provider5()
└──> provider6()
In async mode provider prepares injections asynchronously.
If provider has multiple awaitable dependencies, it will run them concurrently. Provider will wait until all
dependencies are ready and inject them afterwards.
.. code-block:: bash
provider1()
├──> provider2() <── Async mode enabled
├──> provider3() <── Async mode enabled
└──> provider4() <── Async mode enabled
Here is what provider will do for the previous example:
.. code-block:: python
injections = await asyncio.gather(
provider2(),
provider3(),
provider4(),
)
await provider1(*injections)
Overriding behaviour
--------------------
In async mode provider always returns awaitable. It applies to the overriding too. If provider in async mode is
overridden by a provider that doesn't return awaitable result, the result will be wrapped into awaitable.
.. literalinclude:: ../../examples/providers/async_overriding.py
:language: python
:emphasize-lines: 19-24
:lines: 3-
Async mode mechanics and API
----------------------------
By default provider's async mode is undefined.
When provider async mode is undefined, provider will automatically select the mode during the next call.
If the result is awaitable, provider will enable async mode, if not - disable it.
If provider async mode is enabled, provider always returns awaitable. If the result is not awaitable,
provider wraps it into awaitable explicitly. You can safely ``await`` provider in async mode.
If provider async mode is disabled, provider behaves the regular way. It doesn't do async injections
preparation or non-awaitables to awaitables conversion.
Once provider async mode is enabled or disabled, provider will stay in this state. No automatic switching
will be done.
.. image:: images/async_mode.png
You can also use following methods to change provider's async mode manually:
- ``Provider.enable_async_mode()``
- ``Provider.disable_async_mode()``
- ``Provider.reset_async_mode()``
To check the state of provider's async mode use:
- ``Provider.is_async_mode_enabled()``
- ``Provider.is_async_mode_disabled()``
- ``Provider.is_async_mode_undefined()``
See also:
- Wiring :ref:`async-injections-wiring`
- Resource provider :ref:`resource-async-initializers`
- :ref:`fastapi-redis-example`
.. disqus::

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Callable provider
=================
.. meta::
:keywords: Python,DI,Dependency injection,IoC,Inversion of Control,Function,Method,Example
:description: Callable provider helps to make dependencies injection into functions. This page
demonstrates how to use a Callable provider.
Callable providers
------------------
.. currentmodule:: dependency_injector.providers
:py:class:`Callable` provider calls a function, a method or another callable.
:py:class:`Callable` provider calls wrapped callable on every call.
.. literalinclude:: ../../examples/providers/callable.py
Callable providers and injections
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
:py:class:`Callable` provider takes a various number of positional and keyword
arguments that are used as wrapped callable injections. Every time, when
:py:class:`Callable` provider is called, positional and keyword argument
injections would be passed as an callable arguments.
Injections are done according to the next rules:
+ All providers (instances of :py:class:`Provider`) are called every time
when injection needs to be done.
+ Providers could be injected "as is" (delegated), if it is defined obviously.
Check out :ref:`callable_providers_delegation`.
+ All other injectable values are provided *"as is"*.
+ Positional context arguments will be appended after :py:class:`Callable`
positional injections.
+ Keyword context arguments have priority on :py:class:`Callable` keyword
injections and will be merged over them.
Example that shows usage of :py:class:`Callable` with positional argument
injections:
.. literalinclude:: ../../examples/providers/callable_args.py
:language: python
:lines: 3-
:linenos:
``Callable`` provider handles an injection of the dependencies the same way like a
:ref:`factory-provider`.
Next one example shows usage of :py:class:`Callable` with keyword argument
injections:
.. disqus::
.. image:: /images/providers/callable.png
:width: 100%
:align: center
.. literalinclude:: ../../examples/providers/callable_kwargs.py
:language: python
:linenos:
.. _callable_providers_delegation:
Callable providers delegation
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
:py:class:`Callable` provider could be delegated to any other provider via
any kind of injection.
Delegation of :py:class:`Callable` providers is the same as
:py:class:`Factory` providers delegation, please follow
:ref:`factory_providers_delegation` section for examples (with exception
about using :py:class:`DelegatedCallable` instead of
:py:class:`DelegatedFactory`).

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